Exploratory Data Analysis¶

1.Data Preparation¶

!pip install pydicom

Collecting pydicom
  Downloading https://files.pythonhosted.org/packages/f4/15/df16546bc59bfca390cf072d473fb2c8acd4231636f64356593a63137e55/pydicom-2.1.2-py3-none-any.whl (1.9MB)
     |████████████████████████████████| 1.9MB 12.1MB/s 
Installing collected packages: pydicom
Successfully installed pydicom-2.1.2

import glob, pylab, pandas as pd
import pydicom, numpy as np
import random
import matplotlib.pyplot as plt
import seaborn as sns
import multiprocessing
from matplotlib.patches import Rectangle
from imgaug import augmenters as iaa
from sklearn.model_selection import KFold
from tqdm import tqdm
import os
import sys
import json
import math
import cv2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
plt.show()

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

PATH="/content/drive/My Drive/Colab Notebooks/capstone/rsna-pneumonia-detection-challenge"
print(os.listdir(PATH))

['stage_2_detailed_class_info.csv', 'GCP Credits Request Link - RSNA.txt', 'stage_2_sample_submission.csv', 'stage_2_train_labels.csv', 'stage_2_test_images', 'stage_2_train_images', 'train_class_df.csv', 'test_class_df (1).csv', 'test_class_df.csv', 'model_weights.h5', 'model.h5', 'brucechou1983_CheXNet_Keras_0.3.0_weights.h5', 'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5', 'vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5', 'my_model_weights.h5', 'my_model.h5', 'model_vgg_weights.h5', 'model_vgg.h5', 'model_resnet.h5', 'VGG19_&_ResNet_Implementation.ipynb', 'classifier_weights.hdf5', 'chexnet_model.hdf5', 'yolov3_chex_train_30.tf.data-00000-of-00001', 'yolov3_chexnet_model.hdf5', 'model_resnet_weights.h5', 'model_resnet_weights_new.h5', 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', 'resnet50_csv_10.h5', 'RetinaNet.ipynb', 'train_data.csv', 'train.txt', 'JPEG', '.ipynb_checkpoints', 'train_jpg_images', 'pneumonia20201118T1224', 'train_dirs_label.csv', 'train_labels.csv', 'Capstone Project.zip', 'weights', 'working', 'resnet50_csv_50.h5']

train_data = pd.read_csv(PATH+'/stage_2_train_labels.csv')
train_data.shape

(30227, 6)

train_data.head(10)

class_info = pd.read_csv(PATH+'/stage_2_detailed_class_info.csv')
class_info.shape

(30227, 2)

class_info.head(10)

len(train_data.patientId.unique())

26684

# calculating aspect_ratio & area
train_data['aspect_ratio'] = (train_data['width']/train_data['height'])
train_data['area'] = train_data['width'] * train_data['height']

# function to get info from dicom file
def get_dicom_info(patientId, root_dir= PATH+'/stage_2_train_images'):
    fn = os.path.join(root_dir, f'{patientId}.dcm')
    dcm_data = pydicom.read_file(fn)
    return {'age': dcm_data.PatientAge, 
            'gender': dcm_data.PatientSex, 
            'id': os.path.basename(fn).split('.')[0]}

# getting info from dicom file
#patient_ids = list(train_data.patientId.unique())
dicom_info=[]
for i in patient_ids:
    dicom_info.append(get_dicom_info(i))

dicom_info = pd.DataFrame(dicom_info)
dicom_info['gender'] = dicom_info['gender'].astype('category')
dicom_info['age'] = dicom_info['age'].astype(int)

# merging label info with dicom info
#train_data = (train_data.merge(dicom_info, left_on='patientId', right_on='id', how='left')
        .drop(columns='id'))

train_data= pd.read_csv(PATH+'/train_data.csv')
train_data.head()

# saving whole data
#train_data.to_csv('train_data.csv',index=False)

train_data.shape

(30227, 10)

# reading class info
class_info = pd.read_csv(PATH+'/stage_2_detailed_class_info.csv')

# utility functions 

def draw(data):
    """
    Method to draw single patient with bounding box(es) if present 

    """
    # --- Open DICOM file
    d = pydicom.read_file(data['dicom'])
    im = d.pixel_array

    # --- Convert from single-channel grayscale to 3-channel RGB
    im = np.stack([im] * 3, axis=2)

    # --- Add boxes with random color if present
    for box in data['boxes']:
        rgb = np.floor(np.random.rand(3) * 256).astype('int')
        im = overlay_box(im=im, box=box, rgb=rgb, stroke=6)

    pylab.imshow(im, cmap=pylab.cm.gist_gray)
    pylab.axis('off')

def overlay_box(im, box, rgb, stroke=1):
    """
    Method to overlay single box on image

    """
    # --- Convert coordinates to integers
    box = [int(b) for b in box]
    
    # --- Extract coordinates
    y1, x1, height, width = box
    y2 = y1 + height
    x2 = x1 + width

    im[y1:y1 + stroke, x1:x2] = rgb
    im[y2:y2 + stroke, x1:x2] = rgb
    im[y1:y2, x1:x1 + stroke] = rgb
    im[y1:y2, x2:x2 + stroke] = rgb

    return im

def parse_data(df):
    """
    Method to read a CSV file (Pandas dataframe) and parse the 
    data into the following nested dictionary:

      parsed = {
        
        'patientId-00': {
            'dicom': path/to/dicom/file,
            'label': either 0 or 1 for normal or pnuemonia, 
            'boxes': list of box(es)
        },
        'patientId-01': {
            'dicom': path/to/dicom/file,
            'label': either 0 or 1 for normal or pnuemonia, 
            'boxes': list of box(es)
        }, ...

      }

    """
    # --- Define lambda to extract coords in list [y, x, height, width]
    extract_box = lambda row: [row['y'], row['x'], row['height'], row['width']]

    parsed = {}
    for n, row in df.iterrows():
        # --- Initialize patient entry into parsed 
        pid = row['patientId']
        if pid not in parsed:
            parsed[pid] = {
                'dicom': PATH+'/stage_2_train_images/%s.dcm' % pid,
                'label': row['Target'],
                'boxes': []}

        # --- Add box if opacity is present
        if parsed[pid]['label'] == 1:
            parsed[pid]['boxes'].append(extract_box(row))

    return parsed
parsed = parse_data(train_data)
print(parsed['00436515-870c-4b36-a041-de91049b9ab4'])

{'dicom': '/content/drive/My Drive/Colab Notebooks/capstone/rsna-pneumonia-detection-challenge/stage_2_train_images/00436515-870c-4b36-a041-de91049b9ab4.dcm', 'label': 1, 'boxes': [[152.0, 264.0, 379.0, 213.0], [152.0, 562.0, 453.0, 256.0]]}

len(parsed)

26684

train_data['Target'].value_counts()

0    20672
1     9555
Name: Target, dtype: int64

class_info['class'].value_counts()

No Lung Opacity / Not Normal    11821
Lung Opacity                     9555
Normal                           8851
Name: class, dtype: int64

def missing_data(data):
    total = data.isnull().sum().sort_values(ascending = False)
    percent = (data.isnull().sum()/data.isnull().count()*100).sort_values(ascending = False)
    return np.transpose(pd.concat([total, percent], axis=1, keys=['Total', 'Percent']))
missing_data(train_data)

2.Data Overview¶

2.1 Sample DICOM file¶

patientId = train_data['patientId'][0]
dcm_file = PATH+'/stage_2_train_images/%s.dcm' % patientId
dcm_data = pydicom.read_file(dcm_file)

print(dcm_data)

Dataset.file_meta -------------------------------
(0002, 0000) File Meta Information Group Length  UL: 202
(0002, 0001) File Meta Information Version       OB: b'\x00\x01'
(0002, 0002) Media Storage SOP Class UID         UI: Secondary Capture Image Storage
(0002, 0003) Media Storage SOP Instance UID      UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526
(0002, 0010) Transfer Syntax UID                 UI: JPEG Baseline (Process 1)
(0002, 0012) Implementation Class UID            UI: 1.2.276.0.7230010.3.0.3.6.0
(0002, 0013) Implementation Version Name         SH: 'OFFIS_DCMTK_360'
-------------------------------------------------
(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'
(0008, 0016) SOP Class UID                       UI: Secondary Capture Image Storage
(0008, 0018) SOP Instance UID                    UI: 1.2.276.0.7230010.3.1.4.8323329.28530.1517874485.775526
(0008, 0020) Study Date                          DA: '19010101'
(0008, 0030) Study Time                          TM: '000000.00'
(0008, 0050) Accession Number                    SH: ''
(0008, 0060) Modality                            CS: 'CR'
(0008, 0064) Conversion Type                     CS: 'WSD'
(0008, 0090) Referring Physician's Name          PN: ''
(0008, 103e) Series Description                  LO: 'view: PA'
(0010, 0010) Patient's Name                      PN: '0004cfab-14fd-4e49-80ba-63a80b6bddd6'
(0010, 0020) Patient ID                          LO: '0004cfab-14fd-4e49-80ba-63a80b6bddd6'
(0010, 0030) Patient's Birth Date                DA: ''
(0010, 0040) Patient's Sex                       CS: 'F'
(0010, 1010) Patient's Age                       AS: '51'
(0018, 0015) Body Part Examined                  CS: 'CHEST'
(0018, 5101) View Position                       CS: 'PA'
(0020, 000d) Study Instance UID                  UI: 1.2.276.0.7230010.3.1.2.8323329.28530.1517874485.775525
(0020, 000e) Series Instance UID                 UI: 1.2.276.0.7230010.3.1.3.8323329.28530.1517874485.775524
(0020, 0010) Study ID                            SH: ''
(0020, 0011) Series Number                       IS: "1"
(0020, 0013) Instance Number                     IS: "1"
(0020, 0020) Patient Orientation                 CS: ''
(0028, 0002) Samples per Pixel                   US: 1
(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'
(0028, 0010) Rows                                US: 1024
(0028, 0011) Columns                             US: 1024
(0028, 0030) Pixel Spacing                       DS: [0.14300000000000002, 0.14300000000000002]
(0028, 0100) Bits Allocated                      US: 8
(0028, 0101) Bits Stored                         US: 8
(0028, 0102) High Bit                            US: 7
(0028, 0103) Pixel Representation                US: 0
(0028, 2110) Lossy Image Compression             CS: '01'
(0028, 2114) Lossy Image Compression Method      CS: 'ISO_10918_1'
(7fe0, 0010) Pixel Data                          OB: Array of 142006 elements

2.2 Sample CXR image (without opacities)¶

# reading image array from DICOM file
im = dcm_data.pixel_array
pylab.imshow(im, cmap=pylab.cm.gist_gray)
pylab.axis('off')

(-0.5, 1023.5, 1023.5, -0.5)

2.3 CXR image with bounding box around opacities¶

def get_id(data,bbox=1):
    keys = data.keys()
    for i in keys:
        if len(data[i]['boxes']) == bbox:
            return i

pd.DataFrame(parsed)

Image with 1 bounding box

im = draw(parsed[get_id(parsed,bbox=1)])
plt.grid(False)

Image with 2 bounding boxes

im = draw(parsed[get_id(parsed,bbox=2)])
plt.grid(False)

Image with 3 bounding boxes

im = draw(parsed[get_id(parsed,bbox=3)])
plt.grid(False)

Image with 4 bounding boxes

im = draw(parsed[get_id(parsed,bbox=4)])
plt.grid(False)

Observation:-

The minimum number of opacities is 1.
the maximum number of opacities is 4.

detailed_data = pd.concat([train_data, class_info], join='inner', axis=1)
detailed_data.drop(detailed_data.columns[[10]], axis=1, inplace=True)

detailed_data['patientId'] = train_data['patientId']
detailed_data.head()

samplePatientID = list(detailed_data[:3].T.to_dict().values())[0]['patientId']
samplePatientID = samplePatientID+'.dcm'
dicom_file_path = os.path.join(PATH,"stage_2_train_images/",samplePatientID)

def get_boxes_per_patient(df, pId):
    '''
    Given the dataset and one patient ID, 
    return an array of all the bounding boxes and their labels associated with that patient ID.
    Example of return: 
    array([[x1, y1, width1, height1, class1, target1],
           [x2, y2, width2, height2, class2, target2]])
    '''
    
    boxes = df.loc[df['patientId']==pId][['x', 'y', 'width', 'height', 'class', 'Target']].values
    return boxes

def get_dcm_data_per_patient(pId, sample='train'):
    '''
    Given one patient ID and the sample name (train/test), 
    return the corresponding dicom data.
    '''
    return pydicom.read_file(dicom_file_path)

def display_image_per_patient(df, pId, angle=0.0, sample='train'):
    '''
    Given one patient ID and the dataset,
    display the corresponding dicom image with overlaying boxes and class annotation.
    To be implemented: Optionally input the image rotation angle, in case of data augmentation.
    '''
    dcmdata = get_dcm_data_per_patient(pId, sample=sample)
    dcmimg = dcmdata.pixel_array
    boxes = get_boxes_per_patient(df, pId)
    plt.figure(figsize=(20,10))
    plt.imshow(dcmimg, cmap=pylab.cm.binary)
    plt.axis('off')
    
    class_color_dict = {'Normal' : 'green',
                        'No Lung Opacity / Not Normal' : 'orange',
                        'Lung Opacity' : 'red'}

    if len(boxes)>0:
        for box in boxes:
            # extracting individual coordinates and labels
            x, y, w, h, c, t = box 
            # create a rectangle patch
            patch = Rectangle((x,y), w, h, color='red', 
                              angle=angle, fill=False, lw=4, joinstyle='round', alpha=0.6)
            # get current axis and draw rectangle
            plt.gca().add_patch(patch)
            
    # add annotation text
    plt.text(10, 50, c, color=class_color_dict[c], size=20, 
             bbox=dict(edgecolor=class_color_dict[c], facecolor='none', alpha=0.5, lw=2))

pId = detailed_data['patientId'].sample(1).values[0]    
display_image_per_patient(detailed_data, pId, sample='train')

## Plot DICOM images with Target = 1

def show_dicom_images(data):
    img_data = list(data.T.to_dict().values())
    f, ax = plt.subplots(3,3, figsize=(16,18))
    for i,data_row in enumerate(img_data):
        patientImage = data_row['patientId']+'.dcm'
        imagePath = os.path.join(PATH,"stage_2_train_images/",patientImage)
        data_row_img_data = pydicom.read_file(imagePath)
        modality = data_row_img_data.Modality
        age = data_row_img_data.PatientAge
        sex = data_row_img_data.PatientSex
        data_row_img = pydicom.dcmread(imagePath)
        ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) 
        ax[i//3, i%3].axis('off')
        ax[i//3, i%3].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}\nWindow: {}:{}:{}:{}'.format(
                data_row['patientId'],
                modality, age, sex, data_row['Target'], data_row['class'], 
                data_row['x'],data_row['y'],data_row['width'],data_row['height']))
    plt.show()

show_dicom_images(detailed_data[detailed_data['Target']==1].sample(9))

 ## represent the images with the overlay boxes superposed. For this, we will need first to parse the whole dataset with Target = 1  the windows showing a Lung Opacity on the same image

 def show_dicom_images_with_boxes(data):
    img_data = list(data.T.to_dict().values())
    f, ax = plt.subplots(3,3, figsize=(16,18))
    for i,data_row in enumerate(img_data):
        patientImage = data_row['patientId']+'.dcm'
        imagePath = os.path.join(PATH,"stage_2_train_images/",patientImage)
        data_row_img_data = pydicom.read_file(imagePath)
        modality = data_row_img_data.Modality
        age = data_row_img_data.PatientAge
        sex = data_row_img_data.PatientSex
        data_row_img = pydicom.dcmread(imagePath)
        ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) 
        ax[i//3, i%3].axis('off')
        ax[i//3, i%3].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}'.format(
                data_row['patientId'],modality, age, sex, data_row['Target'], data_row['class']))
        rows = detailed_data[detailed_data['patientId']==data_row['patientId']]
        box_data = list(rows.T.to_dict().values())
        for j, row in enumerate(box_data):
            ax[i//3, i%3].add_patch(Rectangle(xy=(row['x'], row['y']),
                        width=row['width'],height=row['height'], 
                        color="yellow",alpha = 0.1))   
    plt.show()

show_dicom_images_with_boxes(detailed_data[detailed_data['Target']==1].sample(9))

show_dicom_images(detailed_data[detailed_data['Target']==0].sample(9))

3.Univariate Analysis¶

3.1 Class Imbalance¶

f, ax = plt.subplots(1,1, figsize=(6,4))
total = float(len(train_data))
sns.countplot(train_data['Target'],order = train_data['Target'].value_counts().index)
for p in ax.patches:
    height = p.get_height()
    ax.text(p.get_x()+p.get_width()/2.,
            height + 3,
            '{:1.2f}%'.format(100*height/total),
            ha="center") 

ax.set_title('Are the classes imbalanced?')
ax.set_xlabel('Has Pneumonia')
ax.set_ylabel('Count')
ax.xaxis.set_tick_params(rotation=0)
plt.show()

/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

Observation:-

From the plot,it is clear that the data is highly imbalanced.
There is almost 5000 positive & 20000 negative points.
The imbalance ratio is almost 4:1 (negative:positive)
The reason behind the imbalance is that there are almost 11000 datapoints which are classified as No Lung Opacity / Not Normal, but those points are also considered as negative.

f, ax = plt.subplots(1,1, figsize=(6,4))
total = float(len(class_info))
sns.countplot(class_info['class'],order = class_info['class'].value_counts().index)
for p in ax.patches:
    height = p.get_height()
    ax.text(p.get_x()+p.get_width()/2.,
            height + 3,
            '{:1.2f}%'.format(100*height/total),
            ha="center") 
plt.show()

/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

plt.figure(figsize=(10,6))
sns.countplot(detailed_data['Target'], hue=detailed_data['class'])

/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

<matplotlib.axes._subplots.AxesSubplot at 0x7f358e14fb38>

3.2 Bounding Box X-Coordinates¶

col = ['x', 'y', 'width', 'height']
plt.figure(figsize=(30,20))

n=1
for i in col:
  plt.subplot(3,3,n)
  sns.distplot(train_data[train_data['Target']==1][i], kde=True)
  plt.axvline(train_data[train_data['Target']==1][i].mean(), linestyle="dashed", label="mean", color='black')
  plt.axvline(train_data[train_data['Target']==1][i].median(), linestyle="dashdot", label="median", color='yellow')
  plt.ylabel("Frequency")
  plt.legend()
  n += 1

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

# function to plot PDF
def plot_pdf(data,x_label,y_label,title):
    f,ax = plt.subplots(1,1, figsize=(7.5,4))
    sns.distplot(a=data, ax=ax)
    plt.xlabel(x_label)
    plt.ylabel(y_label)
    plt.title(title)
    plt.show()

# function to plot CDF
def plot_cdf(data,x_label,y_label,title):
    f,ax = plt.subplots(1,1, figsize=(7.5,4))
    counts, bin_edges = np.histogram(data.dropna(), bins=10, 
                                     density = True)
    pdf = counts/(sum(counts))#cal pdf
    cdf = np.cumsum(pdf)#cumulative sum of pdf,calculating cdf
    plt.plot(bin_edges[1:], cdf)
    plt.ylabel(y_label)
    plt.xlabel(x_label)
    plt.title(title)
    plt.show()
# function to plot box_plot    
def box_plot(data,col,title):
    f,ax = plt.subplots(1,1, figsize=(7.5,4))
    sns.boxplot(y=col, data=data,ax=ax)
    plt.title(title)
    plt.show()

plot_pdf(train_data.x.values,'x-coordinates','Probability Density','PDF of Bounding Box X-coordinates')
plot_cdf(train_data['x'],'X-coordinates','X-coordinates','CDF of Bounding Box X-coordinates')
box_plot(train_data,'x','Boxplot of Bounding Box X-coordinates')

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Observation:-

The double-bell curve that we got in PDF of x-coordinates is due to the location of the lungs (as the opacity bounding boxes are on lungs only).

The x-coordinates passing over the lungs has high values in comparison to the area which do not have lungs.

The range that we got for x-coordinates is from 0 to 800.

Almost 99% of the x-coordinate values are less that 750.

The IQR(interquartile range) that we got is from 200 (25th percentile) & 600(75th percentile).

The median value that we got is 300(approx)

3.3 Bounding Box y-Coordinates¶

plot_pdf(train_data.y.values,'y-coordinates','Probability Density','PDF of Bounding Box y-coordinates')
plot_cdf(train_data['y'],'y-coordinates','Cumulative Density','CDF of Bounding Box y-coordinates')
box_plot(train_data,'y','Boxplot of Bounding Box y-coordinates')

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Observation:-

The decline that we can observe at the middle of the curve is due to the centre of the chest as there is no lungs at the centre (and bounding boxes are only on lungs).

The minimum & maximum value that we got is 0 and 800 respectively.

Almost 99% values are between 100 & 700.

The IQR(inerquartile range) we got is from 250 (25th percentile) & 500 (75th percentile).

The median value that we got is 350 (approx).

There are certain outlier values also above 800.

3.4 Bounding Box width¶

plot_pdf(train_data.width.values,'width','Probability Density','PDF of Bounding Box width')
plot_cdf(train_data['width'],'width','Cumulative Density','CDF of Bounding Box width')
box_plot(train_data,'width','Boxplot of Bounding Box width')

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Observation:-

The PDF of the bounding box width that we got is approx normally distributed.

The minimum & maximum values that we got is 50 & 400 with certain outliers above 400.

Almost 99% values are less than 350.

The IQR(interquartile range) that we got is from 175 to 275 respectively.

The median value that we got is 225 (approx).

3.5 Bounding Box Height¶

plot_pdf(train_data.height.values,'height','Probability Density','PDF of Bounding Box height')
plot_cdf(train_data['height'],'height','Cumulative Density','CDF of Bounding Box height')
box_plot(train_data,'height','Boxplot of Bounding Box height')

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Observation:-

The PDF of the bounding box height that we got is positively skewed.

The minimum & maximum values that we got is 0 & 800 with certain outliers above 800.

Almost 99% values are less than 700.

The IQR(interquartile range) that we got is from 200 to 450 respectively.

The median value that we got is 300 (approx).

3.6 Bounding Box Area¶

plot_pdf(train_data.area.values,'Bounding-Box area','Probability Density','PDF of Bounding Box area')
plot_cdf(train_data['area'],'Bounding-Box area','Cumulative density','CDF of Bounding Box area')
box_plot(train_data,'area','Boxplot of Bounding Box area')

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Observation:-

The PDF of the bounding box bounding-box area that we got is positively skewed.

The general minimum & maximum values that we got is 0 & 200000px (pixels) respectively.

Almost 99% values are less than 250000.

The IQR(interquartile range) that we got is from 25000 to 100000 respectively.

There are certain outliers above 200000.

3.7 Bounding Box Aspect Ratio¶

plot_pdf(train_data.aspect_ratio.values,'Bounding-Box aspect_ratio','Probability Density','PDF of Bounding Box aspect_ratio')
plot_cdf(train_data['aspect_ratio'],'Bounding-Box aspect_ratio','Cumulative density','CDF of Bounding Box aspect_ratio')
box_plot(train_data,'aspect_ratio','Boxplot of Bounding Box aspect_ratio')

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Observation:-

The PDF of the bounding box bounding-box area that we got is positively skewed.

The general minimum & maximum values that we got is 0 & 2 respectively.

Almost 99% values are less than 2.

The IQR(interquartile range) that we got is from 0.5 to 1 respectively.

There are certain outliers above 1.75.

3.8 Patient's Age¶

plot_pdf(train_data.age.values,'Age','Probability Density','PDF of Age')
plot_cdf(train_data['age'],'Age','Cumulative density','CDF of Age')
box_plot(train_data,'age','Boxplot of Age')

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Observation:-

The range of patient's age is from 15 to 95 years.

There are certain values beween 140 and 160 years which are certainly outliers.

The age group from 30 to 95 is highly prone to the disease.

The IQR range is from 30 to 60 years respectively.

The median age of the patients is 50 years.

3.9 Age distribution by gendre and target¶

g = sns.FacetGrid(col='Target', hue='gender', 
                  data=train_data.drop_duplicates(subset=['patientId']), 
                  height=9, palette=dict(F="red", M="blue"))
g.map(sns.distplot, 'age', hist_kws={'alpha': 0.3}).add_legend()
g.fig.suptitle("Age distribution by gender and target", y=1.02, fontsize=20)

/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

Text(0.5, 1.02, 'Age distribution by gender and target')

Observation:-

There is perfect overlap between the distribution of both the genders for both Target=0 & Target=1.

Both the relations looks almost same, it explains fact that both Target = 0 & Target = 1 follows same trend in terms of age.

It explains the fact that there isn't any specif relation between the disease & the gendre.

Even when we compare this target & gendre based plot with the above explained overall age based plot we can easily observe that both are following same trend regardless of gendre & target.

4.Bivariate Analysis¶

4.1 Bounding Box Centroid plot (to detect outliers bounding boxes)¶

tr = train_data[train_data['Target']==1]
centers = (tr.dropna(subset=['x'])
           .assign(center_x=tr.x + tr.width / 2, center_y=tr.y + tr.height / 2))
ax = sns.jointplot("center_x", "center_y", data=centers, height=9, alpha=0.1)
_ = ax.fig.suptitle("Where is Pneumonia located?", y=1.01)

/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning

tmp = detailed_data[detailed_data['Target']==1].sample(3000)

tmp['xc'] = tmp['x'] + tmp['width'] / 2
tmp['yc'] = tmp['y'] + tmp['height'] / 2
tmp['Area'] = tmp['height']*tmp['width']

fig, ax = plt.subplots(1,1,figsize=(7,7))
plt.title("Centers of Lung Opacity rectangles (red) over rectangles (yellow)\nSample size: 3000")
tmp.plot.scatter(x='xc', y='yc', xlim=(0,1024), ylim=(0,1024), ax=ax, alpha=0.8, marker=".", color="red")
for i, crt_sample in tmp.iterrows():
    ax.add_patch(Rectangle(xy=(crt_sample['x'], crt_sample['y']),
                width=crt_sample['width'],height=crt_sample['height'],alpha=3.5e-3, color="yellow"))
plt.show()

tmp = detailed_data.groupby(['patientId','Target', 'class'])['patientId'].count()
df = pd.DataFrame(data={'Exams': tmp.values}, index=tmp.index).reset_index()
tmp = df.groupby(['Exams','Target','class']).count()
df2 = pd.DataFrame(data=tmp.values, index=tmp.index).reset_index()
df2.columns = ['Exams', 'Target','Class', 'Entries']

fig, ax = plt.subplots(nrows=1,figsize=(12,6))
sns.barplot(x = 'Target', y='Entries', hue='Exams',data=df2)
plt.title("Chest exams class and Target")
plt.show()

# getting centres of bounding boxes
centers = (train_data.dropna(subset=['x'])
           .assign(center_x=train_data.x + train_data.width / 2, center_y=train_data.y + train_data.height / 2))

# GaussianMixture for clustering 
from sklearn.mixture import GaussianMixture
clf = GaussianMixture(n_components=2)
clf.fit(centers[['center_x', 'center_y']])
center_probs = clf.predict_proba(centers[['center_x', 'center_y']])
Z = -clf.score_samples(centers[['center_x', 'center_y']])
outliers = centers.iloc[Z > 17]
fig, ax = plt.subplots(figsize=(15,8))
centers.plot.scatter('center_x', 'center_y', c=Z, alpha=0.5, cmap='viridis', ax=ax)
outliers.plot.scatter('center_x', 'center_y', c='red', marker='x', s=100, ax=ax)
_ = ax.set_title('Detecting Outliers Bounding Boxes', fontsize=18)

Observation:-

We can observe that the central area of both the lungs are highly dense, having maximum numbers of bounding box centroids.

As soon as we are moving away from the centre, the bounding box density is gradually decreasing.

The red crosses that we can see on the outskerts of the lungs are outliers.

By removing those outlier values, we can get rid of the effect of outliers.

4.2 Aspect ration vs Area¶

g = sns.relplot(x='area', y='aspect_ratio', 
            data=train_data.dropna(subset=['area', 'aspect_ratio']), 
            height=8, alpha=0.8, aspect=1.4,)
g.fig.suptitle("The relationship between the bounding box's aspect ratio and area", y=1.005)

Text(0.5, 1.005, "The relationship between the bounding box's aspect ratio and area")

Observation:-

The plot depicts an inversly prportional relation between the bounding aspect ratio and area.

As soon as the aspect ratio is increasing the area is decreasingnad vice-versa.

The bounding boxes having maximum heights have small width & vise-varsa.

There are certain bounding boxes having very high apsect ratios.

Feature Engineering & Modelling¶

Preprocessing¶

import pydicom as dicom
import os
import cv2
import PIL # optional
from tqdm import tqdm
import pathlib
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.applications.densenet import DenseNet121
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import f1_score,roc_auc_score
from tensorflow.keras.utils import plot_model,to_categorical
from tensorflow.keras.initializers import he_normal,glorot_normal
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Input, Dropout, Dense, Flatten, Conv2D,MaxPool2D,Dropout,BatchNormalization
from tensorflow.keras.callbacks import TensorBoard,ModelCheckpoint,EarlyStopping,ReduceLROnPlateau
from tensorflow.keras.applications import VGG19,ResNet50
import albumentations as A
import datetime
import random

# to covert dicom files to im images
def dicom_to_jpg(source_folder,destination_folder,labels):
    images_path = os.listdir(source_folder)
    image_dirs_label = {'image_dir':[],'Target':[]}
    for n, image in tqdm(enumerate(images_path)):
        ds = dicom.dcmread(os.path.join(source_folder, image))
        pixel_array_numpy = ds.pixel_array
        image = image.replace('.dcm', '.jpg')
        cv2.imwrite(os.path.join(destination_folder, image), pixel_array_numpy)
        image_dirs_label['image_dir'].append(os.path.join(destination_folder, image))
        image_dirs_label['Target'].append(train_labels[train_labels.patientId== image.split('.')[0]].Target.values[0])
    print('{} dicom files converted to jpg!'.format(len(images_path)))
    return pd.DataFrame(image_dirs_label)

# function to calculate recall
# refernce https://datascience.stackexchange.com/a/45166
def recall_m(y_true, y_pred):
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
        recall = true_positives / (possible_positives + K.epsilon())
        return recall

# function to calculate precision
def precision_m(y_true, y_pred):
        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
        precision = true_positives / (predicted_positives + K.epsilon())
        return precision

# function to calculate micro averaged f1_score
def f1_m(y_true, y_pred):
    precision = precision_m(y_true, y_pred)
    recall = recall_m(y_true, y_pred)
    return 2*((precision*recall)/(precision+recall+K.epsilon()))

# to visualize images
def visualize(image):
    plt.figure(figsize=(10, 10))
    plt.axis('off')
    plt.imshow(image)

# reading train labels & removing duplicates
#train_data = pd.read_csv(PATH+'/stage_2_train_labels.csv')
#train_labels = train_data[['patientId','Target']].drop_duplicates()
#train_labels.to_csv(PATH+'/train_labels.csv',index=False)

# converting dicoms to images
#train_dirs_label = dicom_to_jpg(PATH+"/stage_2_train_images",PATH+"/train_jpg_images",train_labels)

train_dirs_label = pd.read_csv(PATH+'/train_dirs_label.csv')

train_dirs_label.head()

# reading path_label csv
train_dirs_label = pd.read_csv(PATH+'/train_dirs_label.csv',dtype='str')

# shuffeling 
train_dirs_label = train_dirs_label.sample(frac = 1)


datagen=ImageDataGenerator(
        rescale=1./255.,
        rotation_range = 40, 
        shear_range = 0.2, 
        zoom_range = 0.2, 
        horizontal_flip = True,
        validation_split=0.30, 
        brightness_range = (0.5, 1.5) )

train_generator=datagen.flow_from_dataframe(
dataframe=train_dirs_label,
x_col="image_dir",
y_col="Target",
subset="training",
batch_size=4,
seed=42,
shuffle=True,
class_mode="binary",
target_size=(416,416))

valid_generator=datagen.flow_from_dataframe(
dataframe=train_dirs_label,
x_col="image_dir",
y_col="Target",
subset="validation",
batch_size=4,
seed=42,
shuffle=True,
class_mode="binary",
target_size=(416,416))

/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/dataframe_iterator.py:282: UserWarning: Found 302 invalid image filename(s) in x_col="image_dir". These filename(s) will be ignored.
  .format(n_invalid, x_col)

Found 18468 validated image filenames belonging to 2 classes.
Found 7914 validated image filenames belonging to 2 classes.

/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/dataframe_iterator.py:282: UserWarning: Found 302 invalid image filename(s) in x_col="image_dir". These filename(s) will be ignored.
  .format(n_invalid, x_col)

VGG19 & ResNet50 Implementation for Pneumonia Detection¶

# Define the VGG19 model pre-loaded with imagenet weights with last layer set as false
input_shape = (224, 224, 3)
num_of_class=1
img_in = Input(input_shape)       
model = VGG19(include_top= False ,
                weights='/content/drive/My Drive/Colab Notebooks/capstone/rsna-pneumonia-detection-challenge/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5',    
                input_tensor= img_in, 
                input_shape= input_shape,
                pooling ='avg') 

# The pre-trained model has classification output for 14 categories and hence Dense layer is defined with layer 1
x = model.output  
predictions = Dense(1, activation="sigmoid", name="predictions")(x)    
model = Model(inputs=img_in, outputs=predictions)

# Print the model summary
model.summary()

Model: "functional_13"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_7 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
global_average_pooling2d_2 ( (None, 512)               0         
_________________________________________________________________
predictions (Dense)          (None, 1)                 513       
=================================================================
Total params: 20,024,897
Trainable params: 20,024,897
Non-trainable params: 0
_________________________________________________________________

#Set Early stopping parameter and Reduce Learning rate on Plateau
callbacks_list = [EarlyStopping(monitor='val_loss',patience=5,),
                  ModelCheckpoint(filepath='/content/drive/My Drive/Colab Notebooks/capstone/rsna-pneumonia-detection-challenge/my_model_VGG.h5',monitor='val_loss',save_best_only=True,),
                  ReduceLROnPlateau(monitor='val_loss',factor=0.1,patience=2,)]

# Set only the last layer as Trainable
def model_train_layers(model,layer_name):
    model.trainable = True
    set_trainable = False
    for layer in model.layers:
        
        if layer.name == layer_name:
            set_trainable = True
            #print(layer.name)
        if set_trainable:
             layer.trainable = True
        else:
             layer.trainable = False

model_train_layers(model,"predictions")

# Compile with binary cross entropy loss
optimizer = Adam(lr=0.001)
model.compile(optimizer=optimizer, loss="binary_crossentropy", metrics=['acc',f1_m,precision_m, recall_m])

# Run Fit Generator
history=model.fit_generator(generator=train_generator,
                    epochs=3,
                    validation_data=valid_generator,
                    callbacks=callbacks_list)

model.save_weights('model_weights_vgg19.h5')

model.save('model_vgg19.h5')

plt.figure(figsize=(12,4))
plt.subplot(131)
plt.plot(history.epoch, history.history["loss"], label="Train loss")
plt.plot(history.epoch, history.history["val_loss"], label="Valid loss")
plt.legend()
plt.subplot(132)
plt.plot(history.epoch, history.history["acc"], label="Train accuracy")
plt.plot(history.epoch, history.history["val_acc"], label="Valid accuracy")
plt.legend()
plt.subplot(133)
plt.plot(history.epoch, history.history["f1_m"], label="Train f1")
plt.plot(history.epoch, history.history["val_f1_m"], label="Valid f1")
plt.legend()

<matplotlib.legend.Legend at 0x7f1d2e0cbc88>

plt.figure(figsize=(12,4))
plt.subplot(131)
plt.plot(history.epoch, history.history["recall_m"], label="Train recall")
plt.plot(history.epoch, history.history["val_recall_m"], label="Valid recall")
plt.legend()

<matplotlib.legend.Legend at 0x7f1da04aabe0>

model.save_weights("model_vgg_weights.h5")

• Vgg19 has very low f1 score in training. It’s a Basic Image classification model. So, We ignore this model for futher development.

ResNet50¶

# Define the ResNet50 model pre-loaded with imagenet weights with last layer set as false
input_shape = (224, 224, 3)
num_of_class=1
img_in = Input(input_shape)              
model = ResNet50(include_top= False, 
                weights='imagenet',    
                input_tensor= img_in, 
                input_shape= input_shape,
                pooling ='avg')

# The pre-trained model has classification output for 14 categories and hence Dense layer is defined with layer 1
x = model.output 
predictions = Dense(1, activation="sigmoid", name="predictions")(x)    
model = Model(inputs=img_in, outputs=predictions)

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94773248/94765736 [==============================] - 3s 0us/step

model.summary()

Model: "functional_15"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_8 (InputLayer)            [(None, 224, 224, 3) 0                                            
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_8[0][0]                    
__________________________________________________________________________________________________
conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  
__________________________________________________________________________________________________
conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 
__________________________________________________________________________________________________
conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   
__________________________________________________________________________________________________
pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 
__________________________________________________________________________________________________
pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 
__________________________________________________________________________________________________
conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          
                                                                 conv2_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           
                                                                 conv2_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           
                                                                 conv2_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          
                                                                 conv3_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           
                                                                 conv3_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           
                                                                 conv3_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           
                                                                 conv3_block4_3_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          
                                                                 conv4_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           
                                                                 conv4_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           
                                                                 conv4_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           
                                                                 conv4_block4_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           
                                                                 conv4_block5_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           
                                                                 conv4_block6_3_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           
__________________________________________________________________________________________________
conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          
                                                                 conv5_block1_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           
                                                                 conv5_block2_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           
                                                                 conv5_block3_3_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 2048)         0           conv5_block3_out[0][0]           
__________________________________________________________________________________________________
predictions (Dense)             (None, 1)            2049        avg_pool[0][0]                   
==================================================================================================
Total params: 23,589,761
Trainable params: 23,536,641
Non-trainable params: 53,120
__________________________________________________________________________________________________

model_train_layers(model,"predictions")

# Compile with binary cross entropy loss
optimizer = Adam(lr=0.001)
model.compile(optimizer=optimizer, loss="binary_crossentropy", metrics=['acc',f1_m,precision_m, recall_m])

# Run Fit Generator
history=model.fit_generator(generator=train_generator,
                    epochs=3,
                    validation_data=valid_generator,
                    callbacks=callbacks_list)

Epoch 1/3
4617/4617 [==============================] - 1454s 315ms/step - loss: 0.5558 - acc: 0.7698 - f1_m: 0.0076 - precision_m: 0.0072 - recall_m: 0.0100 - val_loss: 0.5231 - val_acc: 0.7695 - val_f1_m: 0.0022 - val_precision_m: 0.0025 - val_recall_m: 0.0020
Epoch 2/3
4617/4617 [==============================] - 1441s 312ms/step - loss: 0.5394 - acc: 0.7684 - f1_m: 0.0143 - precision_m: 0.0142 - recall_m: 0.0176 - val_loss: 0.5268 - val_acc: 0.7698 - val_f1_m: 0.0036 - val_precision_m: 0.0051 - val_recall_m: 0.0029
Epoch 3/3
4617/4617 [==============================] - 1422s 308ms/step - loss: 0.5348 - acc: 0.7710 - f1_m: 0.0230 - precision_m: 0.0247 - recall_m: 0.0253 - val_loss: 0.5039 - val_acc: 0.7698 - val_f1_m: 8.4218e-04 - val_precision_m: 0.0010 - val_recall_m: 7.5796e-04

model.save_weights("model_resnet_weights.h5")

model.save("model_resnet.h5")

#plt.figure(figsize=(15,5))
#plt.subplot(141)
#plt.plot(history.epoch, history.history["loss"], label="Train loss")
#plt.plot(history.epoch, history.history["val_loss"], label="Valid loss")
#plt.legend()
#plt.subplot(142)
#plt.plot(history.epoch, history.history["acc"], label="Train accuracy")
#plt.plot(history.epoch, history.history["val_acc"], label="Valid accuracy")
#plt.legend()
#plt.subplot(143)
#plt.plot(history.epoch, history.history["f1_m"], label="Train f1")
#plt.plot(history.epoch, history.history["val_f1_m"], label="Valid f1")
#plt.legend()
#plt.subplot(144)
#plt.plot(history.epoch, history.history["recall_m"], label="Train recall")
#plt.plot(history.epoch, history.history["val_recall_m"], label="Valid recall")
#plt.legend()

<matplotlib.legend.Legend at 0x7f1deb7f3c18>

• ResNet50 has also low f1 score.

Classification Model (ChexNet i.e DensNet121)¶

# setting up base model 
dense_net_121 = DenseNet121(input_shape=[416,416] + [3],include_top=False,pooling='avg')
base_model_output = Dense(units=14,activation='relu')(dense_net_121.output)
base_model = Model(inputs = dense_net_121.input,outputs=base_model_output)
base_model.load_weights(PATH+'/brucechou1983_CheXNet_Keras_0.3.0_weights.h5')

Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5
29089792/29084464 [==============================] - 1s 0us/step

# freezing initial layers of base model
for layer in base_model.layers[:10]:
    layer.trainable = False

base_model.summary()

Model: "functional_17"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_9 (InputLayer)            [(None, 416, 416, 3) 0                                            
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D)  (None, 422, 422, 3)  0           input_9[0][0]                    
__________________________________________________________________________________________________
conv1/conv (Conv2D)             (None, 208, 208, 64) 9408        zero_padding2d[0][0]             
__________________________________________________________________________________________________
conv1/bn (BatchNormalization)   (None, 208, 208, 64) 256         conv1/conv[0][0]                 
__________________________________________________________________________________________________
conv1/relu (Activation)         (None, 208, 208, 64) 0           conv1/bn[0][0]                   
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 210, 210, 64) 0           conv1/relu[0][0]                 
__________________________________________________________________________________________________
pool1 (MaxPooling2D)            (None, 104, 104, 64) 0           zero_padding2d_1[0][0]           
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 104, 104, 64) 256         pool1[0][0]                      
__________________________________________________________________________________________________
conv2_block1_0_relu (Activation (None, 104, 104, 64) 0           conv2_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D)    (None, 104, 104, 128 8192        conv2_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 104, 104, 128 0           conv2_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_concat (Concatenat (None, 104, 104, 96) 0           pool1[0][0]                      
                                                                 conv2_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_0_bn (BatchNormali (None, 104, 104, 96) 384         conv2_block1_concat[0][0]        
__________________________________________________________________________________________________
conv2_block2_0_relu (Activation (None, 104, 104, 96) 0           conv2_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D)    (None, 104, 104, 128 12288       conv2_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 104, 104, 128 0           conv2_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_concat (Concatenat (None, 104, 104, 128 0           conv2_block1_concat[0][0]        
                                                                 conv2_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_0_bn (BatchNormali (None, 104, 104, 128 512         conv2_block2_concat[0][0]        
__________________________________________________________________________________________________
conv2_block3_0_relu (Activation (None, 104, 104, 128 0           conv2_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D)    (None, 104, 104, 128 16384       conv2_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 104, 104, 128 0           conv2_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_concat (Concatenat (None, 104, 104, 160 0           conv2_block2_concat[0][0]        
                                                                 conv2_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block4_0_bn (BatchNormali (None, 104, 104, 160 640         conv2_block3_concat[0][0]        
__________________________________________________________________________________________________
conv2_block4_0_relu (Activation (None, 104, 104, 160 0           conv2_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block4_1_conv (Conv2D)    (None, 104, 104, 128 20480       conv2_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block4_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block4_1_relu (Activation (None, 104, 104, 128 0           conv2_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block4_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block4_concat (Concatenat (None, 104, 104, 192 0           conv2_block3_concat[0][0]        
                                                                 conv2_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block5_0_bn (BatchNormali (None, 104, 104, 192 768         conv2_block4_concat[0][0]        
__________________________________________________________________________________________________
conv2_block5_0_relu (Activation (None, 104, 104, 192 0           conv2_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block5_1_conv (Conv2D)    (None, 104, 104, 128 24576       conv2_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block5_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block5_1_relu (Activation (None, 104, 104, 128 0           conv2_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block5_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block5_concat (Concatenat (None, 104, 104, 224 0           conv2_block4_concat[0][0]        
                                                                 conv2_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block6_0_bn (BatchNormali (None, 104, 104, 224 896         conv2_block5_concat[0][0]        
__________________________________________________________________________________________________
conv2_block6_0_relu (Activation (None, 104, 104, 224 0           conv2_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block6_1_conv (Conv2D)    (None, 104, 104, 128 28672       conv2_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block6_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block6_1_relu (Activation (None, 104, 104, 128 0           conv2_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block6_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block6_concat (Concatenat (None, 104, 104, 256 0           conv2_block5_concat[0][0]        
                                                                 conv2_block6_2_conv[0][0]        
__________________________________________________________________________________________________
pool2_bn (BatchNormalization)   (None, 104, 104, 256 1024        conv2_block6_concat[0][0]        
__________________________________________________________________________________________________
pool2_relu (Activation)         (None, 104, 104, 256 0           pool2_bn[0][0]                   
__________________________________________________________________________________________________
pool2_conv (Conv2D)             (None, 104, 104, 128 32768       pool2_relu[0][0]                 
__________________________________________________________________________________________________
pool2_pool (AveragePooling2D)   (None, 52, 52, 128)  0           pool2_conv[0][0]                 
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 52, 52, 128)  512         pool2_pool[0][0]                 
__________________________________________________________________________________________________
conv3_block1_0_relu (Activation (None, 52, 52, 128)  0           conv3_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D)    (None, 52, 52, 128)  16384       conv3_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 52, 52, 128)  0           conv3_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_concat (Concatenat (None, 52, 52, 160)  0           pool2_pool[0][0]                 
                                                                 conv3_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_0_bn (BatchNormali (None, 52, 52, 160)  640         conv3_block1_concat[0][0]        
__________________________________________________________________________________________________
conv3_block2_0_relu (Activation (None, 52, 52, 160)  0           conv3_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D)    (None, 52, 52, 128)  20480       conv3_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 52, 52, 128)  0           conv3_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_concat (Concatenat (None, 52, 52, 192)  0           conv3_block1_concat[0][0]        
                                                                 conv3_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_0_bn (BatchNormali (None, 52, 52, 192)  768         conv3_block2_concat[0][0]        
__________________________________________________________________________________________________
conv3_block3_0_relu (Activation (None, 52, 52, 192)  0           conv3_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D)    (None, 52, 52, 128)  24576       conv3_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 52, 52, 128)  0           conv3_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_concat (Concatenat (None, 52, 52, 224)  0           conv3_block2_concat[0][0]        
                                                                 conv3_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_0_bn (BatchNormali (None, 52, 52, 224)  896         conv3_block3_concat[0][0]        
__________________________________________________________________________________________________
conv3_block4_0_relu (Activation (None, 52, 52, 224)  0           conv3_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D)    (None, 52, 52, 128)  28672       conv3_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 52, 52, 128)  0           conv3_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_concat (Concatenat (None, 52, 52, 256)  0           conv3_block3_concat[0][0]        
                                                                 conv3_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block5_0_bn (BatchNormali (None, 52, 52, 256)  1024        conv3_block4_concat[0][0]        
__________________________________________________________________________________________________
conv3_block5_0_relu (Activation (None, 52, 52, 256)  0           conv3_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block5_1_conv (Conv2D)    (None, 52, 52, 128)  32768       conv3_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block5_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block5_1_relu (Activation (None, 52, 52, 128)  0           conv3_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block5_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block5_concat (Concatenat (None, 52, 52, 288)  0           conv3_block4_concat[0][0]        
                                                                 conv3_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block6_0_bn (BatchNormali (None, 52, 52, 288)  1152        conv3_block5_concat[0][0]        
__________________________________________________________________________________________________
conv3_block6_0_relu (Activation (None, 52, 52, 288)  0           conv3_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block6_1_conv (Conv2D)    (None, 52, 52, 128)  36864       conv3_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block6_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block6_1_relu (Activation (None, 52, 52, 128)  0           conv3_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block6_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block6_concat (Concatenat (None, 52, 52, 320)  0           conv3_block5_concat[0][0]        
                                                                 conv3_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block7_0_bn (BatchNormali (None, 52, 52, 320)  1280        conv3_block6_concat[0][0]        
__________________________________________________________________________________________________
conv3_block7_0_relu (Activation (None, 52, 52, 320)  0           conv3_block7_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block7_1_conv (Conv2D)    (None, 52, 52, 128)  40960       conv3_block7_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block7_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block7_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block7_1_relu (Activation (None, 52, 52, 128)  0           conv3_block7_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block7_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block7_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block7_concat (Concatenat (None, 52, 52, 352)  0           conv3_block6_concat[0][0]        
                                                                 conv3_block7_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block8_0_bn (BatchNormali (None, 52, 52, 352)  1408        conv3_block7_concat[0][0]        
__________________________________________________________________________________________________
conv3_block8_0_relu (Activation (None, 52, 52, 352)  0           conv3_block8_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block8_1_conv (Conv2D)    (None, 52, 52, 128)  45056       conv3_block8_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block8_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block8_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block8_1_relu (Activation (None, 52, 52, 128)  0           conv3_block8_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block8_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block8_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block8_concat (Concatenat (None, 52, 52, 384)  0           conv3_block7_concat[0][0]        
                                                                 conv3_block8_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block9_0_bn (BatchNormali (None, 52, 52, 384)  1536        conv3_block8_concat[0][0]        
__________________________________________________________________________________________________
conv3_block9_0_relu (Activation (None, 52, 52, 384)  0           conv3_block9_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block9_1_conv (Conv2D)    (None, 52, 52, 128)  49152       conv3_block9_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block9_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block9_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block9_1_relu (Activation (None, 52, 52, 128)  0           conv3_block9_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block9_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block9_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block9_concat (Concatenat (None, 52, 52, 416)  0           conv3_block8_concat[0][0]        
                                                                 conv3_block9_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block10_0_bn (BatchNormal (None, 52, 52, 416)  1664        conv3_block9_concat[0][0]        
__________________________________________________________________________________________________
conv3_block10_0_relu (Activatio (None, 52, 52, 416)  0           conv3_block10_0_bn[0][0]         
__________________________________________________________________________________________________
conv3_block10_1_conv (Conv2D)   (None, 52, 52, 128)  53248       conv3_block10_0_relu[0][0]       
__________________________________________________________________________________________________
conv3_block10_1_bn (BatchNormal (None, 52, 52, 128)  512         conv3_block10_1_conv[0][0]       
__________________________________________________________________________________________________
conv3_block10_1_relu (Activatio (None, 52, 52, 128)  0           conv3_block10_1_bn[0][0]         
__________________________________________________________________________________________________
conv3_block10_2_conv (Conv2D)   (None, 52, 52, 32)   36864       conv3_block10_1_relu[0][0]       
__________________________________________________________________________________________________
conv3_block10_concat (Concatena (None, 52, 52, 448)  0           conv3_block9_concat[0][0]        
                                                                 conv3_block10_2_conv[0][0]       
__________________________________________________________________________________________________
conv3_block11_0_bn (BatchNormal (None, 52, 52, 448)  1792        conv3_block10_concat[0][0]       
__________________________________________________________________________________________________
conv3_block11_0_relu (Activatio (None, 52, 52, 448)  0           conv3_block11_0_bn[0][0]         
__________________________________________________________________________________________________
conv3_block11_1_conv (Conv2D)   (None, 52, 52, 128)  57344       conv3_block11_0_relu[0][0]       
__________________________________________________________________________________________________
conv3_block11_1_bn (BatchNormal (None, 52, 52, 128)  512         conv3_block11_1_conv[0][0]       
__________________________________________________________________________________________________
conv3_block11_1_relu (Activatio (None, 52, 52, 128)  0           conv3_block11_1_bn[0][0]         
__________________________________________________________________________________________________
conv3_block11_2_conv (Conv2D)   (None, 52, 52, 32)   36864       conv3_block11_1_relu[0][0]       
__________________________________________________________________________________________________
conv3_block11_concat (Concatena (None, 52, 52, 480)  0           conv3_block10_concat[0][0]       
                                                                 conv3_block11_2_conv[0][0]       
__________________________________________________________________________________________________
conv3_block12_0_bn (BatchNormal (None, 52, 52, 480)  1920        conv3_block11_concat[0][0]       
__________________________________________________________________________________________________
conv3_block12_0_relu (Activatio (None, 52, 52, 480)  0           conv3_block12_0_bn[0][0]         
__________________________________________________________________________________________________
conv3_block12_1_conv (Conv2D)   (None, 52, 52, 128)  61440       conv3_block12_0_relu[0][0]       
__________________________________________________________________________________________________
conv3_block12_1_bn (BatchNormal (None, 52, 52, 128)  512         conv3_block12_1_conv[0][0]       
__________________________________________________________________________________________________
conv3_block12_1_relu (Activatio (None, 52, 52, 128)  0           conv3_block12_1_bn[0][0]         
__________________________________________________________________________________________________
conv3_block12_2_conv (Conv2D)   (None, 52, 52, 32)   36864       conv3_block12_1_relu[0][0]       
__________________________________________________________________________________________________
conv3_block12_concat (Concatena (None, 52, 52, 512)  0           conv3_block11_concat[0][0]       
                                                                 conv3_block12_2_conv[0][0]       
__________________________________________________________________________________________________
pool3_bn (BatchNormalization)   (None, 52, 52, 512)  2048        conv3_block12_concat[0][0]       
__________________________________________________________________________________________________
pool3_relu (Activation)         (None, 52, 52, 512)  0           pool3_bn[0][0]                   
__________________________________________________________________________________________________
pool3_conv (Conv2D)             (None, 52, 52, 256)  131072      pool3_relu[0][0]                 
__________________________________________________________________________________________________
pool3_pool (AveragePooling2D)   (None, 26, 26, 256)  0           pool3_conv[0][0]                 
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 26, 26, 256)  1024        pool3_pool[0][0]                 
__________________________________________________________________________________________________
conv4_block1_0_relu (Activation (None, 26, 26, 256)  0           conv4_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D)    (None, 26, 26, 128)  32768       conv4_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 26, 26, 128)  0           conv4_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_concat (Concatenat (None, 26, 26, 288)  0           pool3_pool[0][0]                 
                                                                 conv4_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_0_bn (BatchNormali (None, 26, 26, 288)  1152        conv4_block1_concat[0][0]        
__________________________________________________________________________________________________
conv4_block2_0_relu (Activation (None, 26, 26, 288)  0           conv4_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D)    (None, 26, 26, 128)  36864       conv4_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 26, 26, 128)  0           conv4_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_concat (Concatenat (None, 26, 26, 320)  0           conv4_block1_concat[0][0]        
                                                                 conv4_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_0_bn (BatchNormali (None, 26, 26, 320)  1280        conv4_block2_concat[0][0]        
__________________________________________________________________________________________________
conv4_block3_0_relu (Activation (None, 26, 26, 320)  0           conv4_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D)    (None, 26, 26, 128)  40960       conv4_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 26, 26, 128)  0           conv4_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_concat (Concatenat (None, 26, 26, 352)  0           conv4_block2_concat[0][0]        
                                                                 conv4_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_0_bn (BatchNormali (None, 26, 26, 352)  1408        conv4_block3_concat[0][0]        
__________________________________________________________________________________________________
conv4_block4_0_relu (Activation (None, 26, 26, 352)  0           conv4_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D)    (None, 26, 26, 128)  45056       conv4_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 26, 26, 128)  0           conv4_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_concat (Concatenat (None, 26, 26, 384)  0           conv4_block3_concat[0][0]        
                                                                 conv4_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_0_bn (BatchNormali (None, 26, 26, 384)  1536        conv4_block4_concat[0][0]        
__________________________________________________________________________________________________
conv4_block5_0_relu (Activation (None, 26, 26, 384)  0           conv4_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D)    (None, 26, 26, 128)  49152       conv4_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 26, 26, 128)  0           conv4_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_concat (Concatenat (None, 26, 26, 416)  0           conv4_block4_concat[0][0]        
                                                                 conv4_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_0_bn (BatchNormali (None, 26, 26, 416)  1664        conv4_block5_concat[0][0]        
__________________________________________________________________________________________________
conv4_block6_0_relu (Activation (None, 26, 26, 416)  0           conv4_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D)    (None, 26, 26, 128)  53248       conv4_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 26, 26, 128)  0           conv4_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_concat (Concatenat (None, 26, 26, 448)  0           conv4_block5_concat[0][0]        
                                                                 conv4_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block7_0_bn (BatchNormali (None, 26, 26, 448)  1792        conv4_block6_concat[0][0]        
__________________________________________________________________________________________________
conv4_block7_0_relu (Activation (None, 26, 26, 448)  0           conv4_block7_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block7_1_conv (Conv2D)    (None, 26, 26, 128)  57344       conv4_block7_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block7_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block7_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block7_1_relu (Activation (None, 26, 26, 128)  0           conv4_block7_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block7_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block7_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block7_concat (Concatenat (None, 26, 26, 480)  0           conv4_block6_concat[0][0]        
                                                                 conv4_block7_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block8_0_bn (BatchNormali (None, 26, 26, 480)  1920        conv4_block7_concat[0][0]        
__________________________________________________________________________________________________
conv4_block8_0_relu (Activation (None, 26, 26, 480)  0           conv4_block8_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block8_1_conv (Conv2D)    (None, 26, 26, 128)  61440       conv4_block8_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block8_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block8_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block8_1_relu (Activation (None, 26, 26, 128)  0           conv4_block8_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block8_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block8_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block8_concat (Concatenat (None, 26, 26, 512)  0           conv4_block7_concat[0][0]        
                                                                 conv4_block8_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block9_0_bn (BatchNormali (None, 26, 26, 512)  2048        conv4_block8_concat[0][0]        
__________________________________________________________________________________________________
conv4_block9_0_relu (Activation (None, 26, 26, 512)  0           conv4_block9_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block9_1_conv (Conv2D)    (None, 26, 26, 128)  65536       conv4_block9_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block9_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block9_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block9_1_relu (Activation (None, 26, 26, 128)  0           conv4_block9_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block9_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block9_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block9_concat (Concatenat (None, 26, 26, 544)  0           conv4_block8_concat[0][0]        
                                                                 conv4_block9_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block10_0_bn (BatchNormal (None, 26, 26, 544)  2176        conv4_block9_concat[0][0]        
__________________________________________________________________________________________________
conv4_block10_0_relu (Activatio (None, 26, 26, 544)  0           conv4_block10_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block10_1_conv (Conv2D)   (None, 26, 26, 128)  69632       conv4_block10_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block10_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block10_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block10_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block10_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block10_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block10_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block10_concat (Concatena (None, 26, 26, 576)  0           conv4_block9_concat[0][0]        
                                                                 conv4_block10_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block11_0_bn (BatchNormal (None, 26, 26, 576)  2304        conv4_block10_concat[0][0]       
__________________________________________________________________________________________________
conv4_block11_0_relu (Activatio (None, 26, 26, 576)  0           conv4_block11_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block11_1_conv (Conv2D)   (None, 26, 26, 128)  73728       conv4_block11_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block11_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block11_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block11_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block11_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block11_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block11_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block11_concat (Concatena (None, 26, 26, 608)  0           conv4_block10_concat[0][0]       
                                                                 conv4_block11_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block12_0_bn (BatchNormal (None, 26, 26, 608)  2432        conv4_block11_concat[0][0]       
__________________________________________________________________________________________________
conv4_block12_0_relu (Activatio (None, 26, 26, 608)  0           conv4_block12_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block12_1_conv (Conv2D)   (None, 26, 26, 128)  77824       conv4_block12_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block12_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block12_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block12_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block12_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block12_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block12_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block12_concat (Concatena (None, 26, 26, 640)  0           conv4_block11_concat[0][0]       
                                                                 conv4_block12_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block13_0_bn (BatchNormal (None, 26, 26, 640)  2560        conv4_block12_concat[0][0]       
__________________________________________________________________________________________________
conv4_block13_0_relu (Activatio (None, 26, 26, 640)  0           conv4_block13_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block13_1_conv (Conv2D)   (None, 26, 26, 128)  81920       conv4_block13_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block13_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block13_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block13_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block13_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block13_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block13_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block13_concat (Concatena (None, 26, 26, 672)  0           conv4_block12_concat[0][0]       
                                                                 conv4_block13_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block14_0_bn (BatchNormal (None, 26, 26, 672)  2688        conv4_block13_concat[0][0]       
__________________________________________________________________________________________________
conv4_block14_0_relu (Activatio (None, 26, 26, 672)  0           conv4_block14_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block14_1_conv (Conv2D)   (None, 26, 26, 128)  86016       conv4_block14_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block14_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block14_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block14_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block14_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block14_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block14_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block14_concat (Concatena (None, 26, 26, 704)  0           conv4_block13_concat[0][0]       
                                                                 conv4_block14_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block15_0_bn (BatchNormal (None, 26, 26, 704)  2816        conv4_block14_concat[0][0]       
__________________________________________________________________________________________________
conv4_block15_0_relu (Activatio (None, 26, 26, 704)  0           conv4_block15_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block15_1_conv (Conv2D)   (None, 26, 26, 128)  90112       conv4_block15_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block15_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block15_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block15_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block15_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block15_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block15_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block15_concat (Concatena (None, 26, 26, 736)  0           conv4_block14_concat[0][0]       
                                                                 conv4_block15_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block16_0_bn (BatchNormal (None, 26, 26, 736)  2944        conv4_block15_concat[0][0]       
__________________________________________________________________________________________________
conv4_block16_0_relu (Activatio (None, 26, 26, 736)  0           conv4_block16_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block16_1_conv (Conv2D)   (None, 26, 26, 128)  94208       conv4_block16_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block16_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block16_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block16_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block16_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block16_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block16_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block16_concat (Concatena (None, 26, 26, 768)  0           conv4_block15_concat[0][0]       
                                                                 conv4_block16_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block17_0_bn (BatchNormal (None, 26, 26, 768)  3072        conv4_block16_concat[0][0]       
__________________________________________________________________________________________________
conv4_block17_0_relu (Activatio (None, 26, 26, 768)  0           conv4_block17_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block17_1_conv (Conv2D)   (None, 26, 26, 128)  98304       conv4_block17_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block17_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block17_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block17_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block17_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block17_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block17_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block17_concat (Concatena (None, 26, 26, 800)  0           conv4_block16_concat[0][0]       
                                                                 conv4_block17_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block18_0_bn (BatchNormal (None, 26, 26, 800)  3200        conv4_block17_concat[0][0]       
__________________________________________________________________________________________________
conv4_block18_0_relu (Activatio (None, 26, 26, 800)  0           conv4_block18_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block18_1_conv (Conv2D)   (None, 26, 26, 128)  102400      conv4_block18_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block18_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block18_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block18_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block18_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block18_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block18_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block18_concat (Concatena (None, 26, 26, 832)  0           conv4_block17_concat[0][0]       
                                                                 conv4_block18_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block19_0_bn (BatchNormal (None, 26, 26, 832)  3328        conv4_block18_concat[0][0]       
__________________________________________________________________________________________________
conv4_block19_0_relu (Activatio (None, 26, 26, 832)  0           conv4_block19_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block19_1_conv (Conv2D)   (None, 26, 26, 128)  106496      conv4_block19_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block19_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block19_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block19_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block19_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block19_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block19_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block19_concat (Concatena (None, 26, 26, 864)  0           conv4_block18_concat[0][0]       
                                                                 conv4_block19_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block20_0_bn (BatchNormal (None, 26, 26, 864)  3456        conv4_block19_concat[0][0]       
__________________________________________________________________________________________________
conv4_block20_0_relu (Activatio (None, 26, 26, 864)  0           conv4_block20_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block20_1_conv (Conv2D)   (None, 26, 26, 128)  110592      conv4_block20_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block20_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block20_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block20_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block20_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block20_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block20_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block20_concat (Concatena (None, 26, 26, 896)  0           conv4_block19_concat[0][0]       
                                                                 conv4_block20_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block21_0_bn (BatchNormal (None, 26, 26, 896)  3584        conv4_block20_concat[0][0]       
__________________________________________________________________________________________________
conv4_block21_0_relu (Activatio (None, 26, 26, 896)  0           conv4_block21_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block21_1_conv (Conv2D)   (None, 26, 26, 128)  114688      conv4_block21_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block21_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block21_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block21_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block21_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block21_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block21_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block21_concat (Concatena (None, 26, 26, 928)  0           conv4_block20_concat[0][0]       
                                                                 conv4_block21_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block22_0_bn (BatchNormal (None, 26, 26, 928)  3712        conv4_block21_concat[0][0]       
__________________________________________________________________________________________________
conv4_block22_0_relu (Activatio (None, 26, 26, 928)  0           conv4_block22_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block22_1_conv (Conv2D)   (None, 26, 26, 128)  118784      conv4_block22_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block22_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block22_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block22_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block22_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block22_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block22_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block22_concat (Concatena (None, 26, 26, 960)  0           conv4_block21_concat[0][0]       
                                                                 conv4_block22_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block23_0_bn (BatchNormal (None, 26, 26, 960)  3840        conv4_block22_concat[0][0]       
__________________________________________________________________________________________________
conv4_block23_0_relu (Activatio (None, 26, 26, 960)  0           conv4_block23_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block23_1_conv (Conv2D)   (None, 26, 26, 128)  122880      conv4_block23_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block23_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block23_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block23_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block23_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block23_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block23_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block23_concat (Concatena (None, 26, 26, 992)  0           conv4_block22_concat[0][0]       
                                                                 conv4_block23_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block24_0_bn (BatchNormal (None, 26, 26, 992)  3968        conv4_block23_concat[0][0]       
__________________________________________________________________________________________________
conv4_block24_0_relu (Activatio (None, 26, 26, 992)  0           conv4_block24_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block24_1_conv (Conv2D)   (None, 26, 26, 128)  126976      conv4_block24_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block24_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block24_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block24_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block24_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block24_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block24_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block24_concat (Concatena (None, 26, 26, 1024) 0           conv4_block23_concat[0][0]       
                                                                 conv4_block24_2_conv[0][0]       
__________________________________________________________________________________________________
pool4_bn (BatchNormalization)   (None, 26, 26, 1024) 4096        conv4_block24_concat[0][0]       
__________________________________________________________________________________________________
pool4_relu (Activation)         (None, 26, 26, 1024) 0           pool4_bn[0][0]                   
__________________________________________________________________________________________________
pool4_conv (Conv2D)             (None, 26, 26, 512)  524288      pool4_relu[0][0]                 
__________________________________________________________________________________________________
pool4_pool (AveragePooling2D)   (None, 13, 13, 512)  0           pool4_conv[0][0]                 
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 13, 13, 512)  2048        pool4_pool[0][0]                 
__________________________________________________________________________________________________
conv5_block1_0_relu (Activation (None, 13, 13, 512)  0           conv5_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D)    (None, 13, 13, 128)  65536       conv5_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 13, 13, 128)  0           conv5_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_concat (Concatenat (None, 13, 13, 544)  0           pool4_pool[0][0]                 
                                                                 conv5_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_0_bn (BatchNormali (None, 13, 13, 544)  2176        conv5_block1_concat[0][0]        
__________________________________________________________________________________________________
conv5_block2_0_relu (Activation (None, 13, 13, 544)  0           conv5_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D)    (None, 13, 13, 128)  69632       conv5_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 13, 13, 128)  0           conv5_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_concat (Concatenat (None, 13, 13, 576)  0           conv5_block1_concat[0][0]        
                                                                 conv5_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_0_bn (BatchNormali (None, 13, 13, 576)  2304        conv5_block2_concat[0][0]        
__________________________________________________________________________________________________
conv5_block3_0_relu (Activation (None, 13, 13, 576)  0           conv5_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D)    (None, 13, 13, 128)  73728       conv5_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 13, 13, 128)  0           conv5_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_concat (Concatenat (None, 13, 13, 608)  0           conv5_block2_concat[0][0]        
                                                                 conv5_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block4_0_bn (BatchNormali (None, 13, 13, 608)  2432        conv5_block3_concat[0][0]        
__________________________________________________________________________________________________
conv5_block4_0_relu (Activation (None, 13, 13, 608)  0           conv5_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block4_1_conv (Conv2D)    (None, 13, 13, 128)  77824       conv5_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block4_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block4_1_relu (Activation (None, 13, 13, 128)  0           conv5_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block4_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block4_concat (Concatenat (None, 13, 13, 640)  0           conv5_block3_concat[0][0]        
                                                                 conv5_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block5_0_bn (BatchNormali (None, 13, 13, 640)  2560        conv5_block4_concat[0][0]        
__________________________________________________________________________________________________
conv5_block5_0_relu (Activation (None, 13, 13, 640)  0           conv5_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block5_1_conv (Conv2D)    (None, 13, 13, 128)  81920       conv5_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block5_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block5_1_relu (Activation (None, 13, 13, 128)  0           conv5_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block5_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block5_concat (Concatenat (None, 13, 13, 672)  0           conv5_block4_concat[0][0]        
                                                                 conv5_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block6_0_bn (BatchNormali (None, 13, 13, 672)  2688        conv5_block5_concat[0][0]        
__________________________________________________________________________________________________
conv5_block6_0_relu (Activation (None, 13, 13, 672)  0           conv5_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block6_1_conv (Conv2D)    (None, 13, 13, 128)  86016       conv5_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block6_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block6_1_relu (Activation (None, 13, 13, 128)  0           conv5_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block6_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block6_concat (Concatenat (None, 13, 13, 704)  0           conv5_block5_concat[0][0]        
                                                                 conv5_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block7_0_bn (BatchNormali (None, 13, 13, 704)  2816        conv5_block6_concat[0][0]        
__________________________________________________________________________________________________
conv5_block7_0_relu (Activation (None, 13, 13, 704)  0           conv5_block7_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block7_1_conv (Conv2D)    (None, 13, 13, 128)  90112       conv5_block7_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block7_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block7_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block7_1_relu (Activation (None, 13, 13, 128)  0           conv5_block7_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block7_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block7_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block7_concat (Concatenat (None, 13, 13, 736)  0           conv5_block6_concat[0][0]        
                                                                 conv5_block7_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block8_0_bn (BatchNormali (None, 13, 13, 736)  2944        conv5_block7_concat[0][0]        
__________________________________________________________________________________________________
conv5_block8_0_relu (Activation (None, 13, 13, 736)  0           conv5_block8_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block8_1_conv (Conv2D)    (None, 13, 13, 128)  94208       conv5_block8_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block8_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block8_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block8_1_relu (Activation (None, 13, 13, 128)  0           conv5_block8_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block8_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block8_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block8_concat (Concatenat (None, 13, 13, 768)  0           conv5_block7_concat[0][0]        
                                                                 conv5_block8_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block9_0_bn (BatchNormali (None, 13, 13, 768)  3072        conv5_block8_concat[0][0]        
__________________________________________________________________________________________________
conv5_block9_0_relu (Activation (None, 13, 13, 768)  0           conv5_block9_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block9_1_conv (Conv2D)    (None, 13, 13, 128)  98304       conv5_block9_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block9_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block9_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block9_1_relu (Activation (None, 13, 13, 128)  0           conv5_block9_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block9_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block9_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block9_concat (Concatenat (None, 13, 13, 800)  0           conv5_block8_concat[0][0]        
                                                                 conv5_block9_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block10_0_bn (BatchNormal (None, 13, 13, 800)  3200        conv5_block9_concat[0][0]        
__________________________________________________________________________________________________
conv5_block10_0_relu (Activatio (None, 13, 13, 800)  0           conv5_block10_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block10_1_conv (Conv2D)   (None, 13, 13, 128)  102400      conv5_block10_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block10_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block10_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block10_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block10_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block10_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block10_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block10_concat (Concatena (None, 13, 13, 832)  0           conv5_block9_concat[0][0]        
                                                                 conv5_block10_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block11_0_bn (BatchNormal (None, 13, 13, 832)  3328        conv5_block10_concat[0][0]       
__________________________________________________________________________________________________
conv5_block11_0_relu (Activatio (None, 13, 13, 832)  0           conv5_block11_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block11_1_conv (Conv2D)   (None, 13, 13, 128)  106496      conv5_block11_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block11_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block11_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block11_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block11_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block11_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block11_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block11_concat (Concatena (None, 13, 13, 864)  0           conv5_block10_concat[0][0]       
                                                                 conv5_block11_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block12_0_bn (BatchNormal (None, 13, 13, 864)  3456        conv5_block11_concat[0][0]       
__________________________________________________________________________________________________
conv5_block12_0_relu (Activatio (None, 13, 13, 864)  0           conv5_block12_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block12_1_conv (Conv2D)   (None, 13, 13, 128)  110592      conv5_block12_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block12_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block12_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block12_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block12_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block12_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block12_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block12_concat (Concatena (None, 13, 13, 896)  0           conv5_block11_concat[0][0]       
                                                                 conv5_block12_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block13_0_bn (BatchNormal (None, 13, 13, 896)  3584        conv5_block12_concat[0][0]       
__________________________________________________________________________________________________
conv5_block13_0_relu (Activatio (None, 13, 13, 896)  0           conv5_block13_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block13_1_conv (Conv2D)   (None, 13, 13, 128)  114688      conv5_block13_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block13_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block13_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block13_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block13_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block13_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block13_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block13_concat (Concatena (None, 13, 13, 928)  0           conv5_block12_concat[0][0]       
                                                                 conv5_block13_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block14_0_bn (BatchNormal (None, 13, 13, 928)  3712        conv5_block13_concat[0][0]       
__________________________________________________________________________________________________
conv5_block14_0_relu (Activatio (None, 13, 13, 928)  0           conv5_block14_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block14_1_conv (Conv2D)   (None, 13, 13, 128)  118784      conv5_block14_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block14_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block14_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block14_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block14_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block14_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block14_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block14_concat (Concatena (None, 13, 13, 960)  0           conv5_block13_concat[0][0]       
                                                                 conv5_block14_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block15_0_bn (BatchNormal (None, 13, 13, 960)  3840        conv5_block14_concat[0][0]       
__________________________________________________________________________________________________
conv5_block15_0_relu (Activatio (None, 13, 13, 960)  0           conv5_block15_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block15_1_conv (Conv2D)   (None, 13, 13, 128)  122880      conv5_block15_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block15_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block15_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block15_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block15_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block15_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block15_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block15_concat (Concatena (None, 13, 13, 992)  0           conv5_block14_concat[0][0]       
                                                                 conv5_block15_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block16_0_bn (BatchNormal (None, 13, 13, 992)  3968        conv5_block15_concat[0][0]       
__________________________________________________________________________________________________
conv5_block16_0_relu (Activatio (None, 13, 13, 992)  0           conv5_block16_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block16_1_conv (Conv2D)   (None, 13, 13, 128)  126976      conv5_block16_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block16_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block16_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block16_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block16_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block16_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block16_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block16_concat (Concatena (None, 13, 13, 1024) 0           conv5_block15_concat[0][0]       
                                                                 conv5_block16_2_conv[0][0]       
__________________________________________________________________________________________________
bn (BatchNormalization)         (None, 13, 13, 1024) 4096        conv5_block16_concat[0][0]       
__________________________________________________________________________________________________
relu (Activation)               (None, 13, 13, 1024) 0           bn[0][0]                         
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 1024)         0           relu[0][0]                       
__________________________________________________________________________________________________
dense (Dense)                   (None, 14)           14350       avg_pool[0][0]                   
==================================================================================================
Total params: 7,051,854
Trainable params: 6,950,350
Non-trainable params: 101,504
__________________________________________________________________________________________________

# setting up final model 
output_layer = Dense(1,activation='sigmoid')(base_model.layers[-2].output)
model = Model(inputs=base_model.inputs, outputs=output_layer)
model.compile(optimizer='adam', loss='binary_crossentropy',metrics = ['accuracy',f1_m])

model.summary()

Model: "functional_19"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_9 (InputLayer)            [(None, 416, 416, 3) 0                                            
__________________________________________________________________________________________________
zero_padding2d (ZeroPadding2D)  (None, 422, 422, 3)  0           input_9[0][0]                    
__________________________________________________________________________________________________
conv1/conv (Conv2D)             (None, 208, 208, 64) 9408        zero_padding2d[0][0]             
__________________________________________________________________________________________________
conv1/bn (BatchNormalization)   (None, 208, 208, 64) 256         conv1/conv[0][0]                 
__________________________________________________________________________________________________
conv1/relu (Activation)         (None, 208, 208, 64) 0           conv1/bn[0][0]                   
__________________________________________________________________________________________________
zero_padding2d_1 (ZeroPadding2D (None, 210, 210, 64) 0           conv1/relu[0][0]                 
__________________________________________________________________________________________________
pool1 (MaxPooling2D)            (None, 104, 104, 64) 0           zero_padding2d_1[0][0]           
__________________________________________________________________________________________________
conv2_block1_0_bn (BatchNormali (None, 104, 104, 64) 256         pool1[0][0]                      
__________________________________________________________________________________________________
conv2_block1_0_relu (Activation (None, 104, 104, 64) 0           conv2_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_1_conv (Conv2D)    (None, 104, 104, 128 8192        conv2_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block1_1_relu (Activation (None, 104, 104, 128 0           conv2_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block1_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block1_concat (Concatenat (None, 104, 104, 96) 0           pool1[0][0]                      
                                                                 conv2_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_0_bn (BatchNormali (None, 104, 104, 96) 384         conv2_block1_concat[0][0]        
__________________________________________________________________________________________________
conv2_block2_0_relu (Activation (None, 104, 104, 96) 0           conv2_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_1_conv (Conv2D)    (None, 104, 104, 128 12288       conv2_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block2_1_relu (Activation (None, 104, 104, 128 0           conv2_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block2_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block2_concat (Concatenat (None, 104, 104, 128 0           conv2_block1_concat[0][0]        
                                                                 conv2_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_0_bn (BatchNormali (None, 104, 104, 128 512         conv2_block2_concat[0][0]        
__________________________________________________________________________________________________
conv2_block3_0_relu (Activation (None, 104, 104, 128 0           conv2_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_1_conv (Conv2D)    (None, 104, 104, 128 16384       conv2_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block3_1_relu (Activation (None, 104, 104, 128 0           conv2_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block3_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block3_concat (Concatenat (None, 104, 104, 160 0           conv2_block2_concat[0][0]        
                                                                 conv2_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block4_0_bn (BatchNormali (None, 104, 104, 160 640         conv2_block3_concat[0][0]        
__________________________________________________________________________________________________
conv2_block4_0_relu (Activation (None, 104, 104, 160 0           conv2_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block4_1_conv (Conv2D)    (None, 104, 104, 128 20480       conv2_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block4_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block4_1_relu (Activation (None, 104, 104, 128 0           conv2_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block4_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block4_concat (Concatenat (None, 104, 104, 192 0           conv2_block3_concat[0][0]        
                                                                 conv2_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block5_0_bn (BatchNormali (None, 104, 104, 192 768         conv2_block4_concat[0][0]        
__________________________________________________________________________________________________
conv2_block5_0_relu (Activation (None, 104, 104, 192 0           conv2_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block5_1_conv (Conv2D)    (None, 104, 104, 128 24576       conv2_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block5_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block5_1_relu (Activation (None, 104, 104, 128 0           conv2_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block5_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block5_concat (Concatenat (None, 104, 104, 224 0           conv2_block4_concat[0][0]        
                                                                 conv2_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv2_block6_0_bn (BatchNormali (None, 104, 104, 224 896         conv2_block5_concat[0][0]        
__________________________________________________________________________________________________
conv2_block6_0_relu (Activation (None, 104, 104, 224 0           conv2_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv2_block6_1_conv (Conv2D)    (None, 104, 104, 128 28672       conv2_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv2_block6_1_bn (BatchNormali (None, 104, 104, 128 512         conv2_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv2_block6_1_relu (Activation (None, 104, 104, 128 0           conv2_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv2_block6_2_conv (Conv2D)    (None, 104, 104, 32) 36864       conv2_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv2_block6_concat (Concatenat (None, 104, 104, 256 0           conv2_block5_concat[0][0]        
                                                                 conv2_block6_2_conv[0][0]        
__________________________________________________________________________________________________
pool2_bn (BatchNormalization)   (None, 104, 104, 256 1024        conv2_block6_concat[0][0]        
__________________________________________________________________________________________________
pool2_relu (Activation)         (None, 104, 104, 256 0           pool2_bn[0][0]                   
__________________________________________________________________________________________________
pool2_conv (Conv2D)             (None, 104, 104, 128 32768       pool2_relu[0][0]                 
__________________________________________________________________________________________________
pool2_pool (AveragePooling2D)   (None, 52, 52, 128)  0           pool2_conv[0][0]                 
__________________________________________________________________________________________________
conv3_block1_0_bn (BatchNormali (None, 52, 52, 128)  512         pool2_pool[0][0]                 
__________________________________________________________________________________________________
conv3_block1_0_relu (Activation (None, 52, 52, 128)  0           conv3_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_1_conv (Conv2D)    (None, 52, 52, 128)  16384       conv3_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block1_1_relu (Activation (None, 52, 52, 128)  0           conv3_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block1_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block1_concat (Concatenat (None, 52, 52, 160)  0           pool2_pool[0][0]                 
                                                                 conv3_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_0_bn (BatchNormali (None, 52, 52, 160)  640         conv3_block1_concat[0][0]        
__________________________________________________________________________________________________
conv3_block2_0_relu (Activation (None, 52, 52, 160)  0           conv3_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_1_conv (Conv2D)    (None, 52, 52, 128)  20480       conv3_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block2_1_relu (Activation (None, 52, 52, 128)  0           conv3_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block2_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block2_concat (Concatenat (None, 52, 52, 192)  0           conv3_block1_concat[0][0]        
                                                                 conv3_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_0_bn (BatchNormali (None, 52, 52, 192)  768         conv3_block2_concat[0][0]        
__________________________________________________________________________________________________
conv3_block3_0_relu (Activation (None, 52, 52, 192)  0           conv3_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_1_conv (Conv2D)    (None, 52, 52, 128)  24576       conv3_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block3_1_relu (Activation (None, 52, 52, 128)  0           conv3_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block3_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block3_concat (Concatenat (None, 52, 52, 224)  0           conv3_block2_concat[0][0]        
                                                                 conv3_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_0_bn (BatchNormali (None, 52, 52, 224)  896         conv3_block3_concat[0][0]        
__________________________________________________________________________________________________
conv3_block4_0_relu (Activation (None, 52, 52, 224)  0           conv3_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_1_conv (Conv2D)    (None, 52, 52, 128)  28672       conv3_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block4_1_relu (Activation (None, 52, 52, 128)  0           conv3_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block4_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block4_concat (Concatenat (None, 52, 52, 256)  0           conv3_block3_concat[0][0]        
                                                                 conv3_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block5_0_bn (BatchNormali (None, 52, 52, 256)  1024        conv3_block4_concat[0][0]        
__________________________________________________________________________________________________
conv3_block5_0_relu (Activation (None, 52, 52, 256)  0           conv3_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block5_1_conv (Conv2D)    (None, 52, 52, 128)  32768       conv3_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block5_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block5_1_relu (Activation (None, 52, 52, 128)  0           conv3_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block5_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block5_concat (Concatenat (None, 52, 52, 288)  0           conv3_block4_concat[0][0]        
                                                                 conv3_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block6_0_bn (BatchNormali (None, 52, 52, 288)  1152        conv3_block5_concat[0][0]        
__________________________________________________________________________________________________
conv3_block6_0_relu (Activation (None, 52, 52, 288)  0           conv3_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block6_1_conv (Conv2D)    (None, 52, 52, 128)  36864       conv3_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block6_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block6_1_relu (Activation (None, 52, 52, 128)  0           conv3_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block6_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block6_concat (Concatenat (None, 52, 52, 320)  0           conv3_block5_concat[0][0]        
                                                                 conv3_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block7_0_bn (BatchNormali (None, 52, 52, 320)  1280        conv3_block6_concat[0][0]        
__________________________________________________________________________________________________
conv3_block7_0_relu (Activation (None, 52, 52, 320)  0           conv3_block7_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block7_1_conv (Conv2D)    (None, 52, 52, 128)  40960       conv3_block7_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block7_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block7_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block7_1_relu (Activation (None, 52, 52, 128)  0           conv3_block7_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block7_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block7_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block7_concat (Concatenat (None, 52, 52, 352)  0           conv3_block6_concat[0][0]        
                                                                 conv3_block7_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block8_0_bn (BatchNormali (None, 52, 52, 352)  1408        conv3_block7_concat[0][0]        
__________________________________________________________________________________________________
conv3_block8_0_relu (Activation (None, 52, 52, 352)  0           conv3_block8_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block8_1_conv (Conv2D)    (None, 52, 52, 128)  45056       conv3_block8_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block8_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block8_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block8_1_relu (Activation (None, 52, 52, 128)  0           conv3_block8_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block8_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block8_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block8_concat (Concatenat (None, 52, 52, 384)  0           conv3_block7_concat[0][0]        
                                                                 conv3_block8_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block9_0_bn (BatchNormali (None, 52, 52, 384)  1536        conv3_block8_concat[0][0]        
__________________________________________________________________________________________________
conv3_block9_0_relu (Activation (None, 52, 52, 384)  0           conv3_block9_0_bn[0][0]          
__________________________________________________________________________________________________
conv3_block9_1_conv (Conv2D)    (None, 52, 52, 128)  49152       conv3_block9_0_relu[0][0]        
__________________________________________________________________________________________________
conv3_block9_1_bn (BatchNormali (None, 52, 52, 128)  512         conv3_block9_1_conv[0][0]        
__________________________________________________________________________________________________
conv3_block9_1_relu (Activation (None, 52, 52, 128)  0           conv3_block9_1_bn[0][0]          
__________________________________________________________________________________________________
conv3_block9_2_conv (Conv2D)    (None, 52, 52, 32)   36864       conv3_block9_1_relu[0][0]        
__________________________________________________________________________________________________
conv3_block9_concat (Concatenat (None, 52, 52, 416)  0           conv3_block8_concat[0][0]        
                                                                 conv3_block9_2_conv[0][0]        
__________________________________________________________________________________________________
conv3_block10_0_bn (BatchNormal (None, 52, 52, 416)  1664        conv3_block9_concat[0][0]        
__________________________________________________________________________________________________
conv3_block10_0_relu (Activatio (None, 52, 52, 416)  0           conv3_block10_0_bn[0][0]         
__________________________________________________________________________________________________
conv3_block10_1_conv (Conv2D)   (None, 52, 52, 128)  53248       conv3_block10_0_relu[0][0]       
__________________________________________________________________________________________________
conv3_block10_1_bn (BatchNormal (None, 52, 52, 128)  512         conv3_block10_1_conv[0][0]       
__________________________________________________________________________________________________
conv3_block10_1_relu (Activatio (None, 52, 52, 128)  0           conv3_block10_1_bn[0][0]         
__________________________________________________________________________________________________
conv3_block10_2_conv (Conv2D)   (None, 52, 52, 32)   36864       conv3_block10_1_relu[0][0]       
__________________________________________________________________________________________________
conv3_block10_concat (Concatena (None, 52, 52, 448)  0           conv3_block9_concat[0][0]        
                                                                 conv3_block10_2_conv[0][0]       
__________________________________________________________________________________________________
conv3_block11_0_bn (BatchNormal (None, 52, 52, 448)  1792        conv3_block10_concat[0][0]       
__________________________________________________________________________________________________
conv3_block11_0_relu (Activatio (None, 52, 52, 448)  0           conv3_block11_0_bn[0][0]         
__________________________________________________________________________________________________
conv3_block11_1_conv (Conv2D)   (None, 52, 52, 128)  57344       conv3_block11_0_relu[0][0]       
__________________________________________________________________________________________________
conv3_block11_1_bn (BatchNormal (None, 52, 52, 128)  512         conv3_block11_1_conv[0][0]       
__________________________________________________________________________________________________
conv3_block11_1_relu (Activatio (None, 52, 52, 128)  0           conv3_block11_1_bn[0][0]         
__________________________________________________________________________________________________
conv3_block11_2_conv (Conv2D)   (None, 52, 52, 32)   36864       conv3_block11_1_relu[0][0]       
__________________________________________________________________________________________________
conv3_block11_concat (Concatena (None, 52, 52, 480)  0           conv3_block10_concat[0][0]       
                                                                 conv3_block11_2_conv[0][0]       
__________________________________________________________________________________________________
conv3_block12_0_bn (BatchNormal (None, 52, 52, 480)  1920        conv3_block11_concat[0][0]       
__________________________________________________________________________________________________
conv3_block12_0_relu (Activatio (None, 52, 52, 480)  0           conv3_block12_0_bn[0][0]         
__________________________________________________________________________________________________
conv3_block12_1_conv (Conv2D)   (None, 52, 52, 128)  61440       conv3_block12_0_relu[0][0]       
__________________________________________________________________________________________________
conv3_block12_1_bn (BatchNormal (None, 52, 52, 128)  512         conv3_block12_1_conv[0][0]       
__________________________________________________________________________________________________
conv3_block12_1_relu (Activatio (None, 52, 52, 128)  0           conv3_block12_1_bn[0][0]         
__________________________________________________________________________________________________
conv3_block12_2_conv (Conv2D)   (None, 52, 52, 32)   36864       conv3_block12_1_relu[0][0]       
__________________________________________________________________________________________________
conv3_block12_concat (Concatena (None, 52, 52, 512)  0           conv3_block11_concat[0][0]       
                                                                 conv3_block12_2_conv[0][0]       
__________________________________________________________________________________________________
pool3_bn (BatchNormalization)   (None, 52, 52, 512)  2048        conv3_block12_concat[0][0]       
__________________________________________________________________________________________________
pool3_relu (Activation)         (None, 52, 52, 512)  0           pool3_bn[0][0]                   
__________________________________________________________________________________________________
pool3_conv (Conv2D)             (None, 52, 52, 256)  131072      pool3_relu[0][0]                 
__________________________________________________________________________________________________
pool3_pool (AveragePooling2D)   (None, 26, 26, 256)  0           pool3_conv[0][0]                 
__________________________________________________________________________________________________
conv4_block1_0_bn (BatchNormali (None, 26, 26, 256)  1024        pool3_pool[0][0]                 
__________________________________________________________________________________________________
conv4_block1_0_relu (Activation (None, 26, 26, 256)  0           conv4_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_1_conv (Conv2D)    (None, 26, 26, 128)  32768       conv4_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block1_1_relu (Activation (None, 26, 26, 128)  0           conv4_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block1_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block1_concat (Concatenat (None, 26, 26, 288)  0           pool3_pool[0][0]                 
                                                                 conv4_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_0_bn (BatchNormali (None, 26, 26, 288)  1152        conv4_block1_concat[0][0]        
__________________________________________________________________________________________________
conv4_block2_0_relu (Activation (None, 26, 26, 288)  0           conv4_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_1_conv (Conv2D)    (None, 26, 26, 128)  36864       conv4_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block2_1_relu (Activation (None, 26, 26, 128)  0           conv4_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block2_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block2_concat (Concatenat (None, 26, 26, 320)  0           conv4_block1_concat[0][0]        
                                                                 conv4_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_0_bn (BatchNormali (None, 26, 26, 320)  1280        conv4_block2_concat[0][0]        
__________________________________________________________________________________________________
conv4_block3_0_relu (Activation (None, 26, 26, 320)  0           conv4_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_1_conv (Conv2D)    (None, 26, 26, 128)  40960       conv4_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block3_1_relu (Activation (None, 26, 26, 128)  0           conv4_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block3_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block3_concat (Concatenat (None, 26, 26, 352)  0           conv4_block2_concat[0][0]        
                                                                 conv4_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_0_bn (BatchNormali (None, 26, 26, 352)  1408        conv4_block3_concat[0][0]        
__________________________________________________________________________________________________
conv4_block4_0_relu (Activation (None, 26, 26, 352)  0           conv4_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_1_conv (Conv2D)    (None, 26, 26, 128)  45056       conv4_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block4_1_relu (Activation (None, 26, 26, 128)  0           conv4_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block4_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block4_concat (Concatenat (None, 26, 26, 384)  0           conv4_block3_concat[0][0]        
                                                                 conv4_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_0_bn (BatchNormali (None, 26, 26, 384)  1536        conv4_block4_concat[0][0]        
__________________________________________________________________________________________________
conv4_block5_0_relu (Activation (None, 26, 26, 384)  0           conv4_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_1_conv (Conv2D)    (None, 26, 26, 128)  49152       conv4_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block5_1_relu (Activation (None, 26, 26, 128)  0           conv4_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block5_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block5_concat (Concatenat (None, 26, 26, 416)  0           conv4_block4_concat[0][0]        
                                                                 conv4_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_0_bn (BatchNormali (None, 26, 26, 416)  1664        conv4_block5_concat[0][0]        
__________________________________________________________________________________________________
conv4_block6_0_relu (Activation (None, 26, 26, 416)  0           conv4_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_1_conv (Conv2D)    (None, 26, 26, 128)  53248       conv4_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block6_1_relu (Activation (None, 26, 26, 128)  0           conv4_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block6_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block6_concat (Concatenat (None, 26, 26, 448)  0           conv4_block5_concat[0][0]        
                                                                 conv4_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block7_0_bn (BatchNormali (None, 26, 26, 448)  1792        conv4_block6_concat[0][0]        
__________________________________________________________________________________________________
conv4_block7_0_relu (Activation (None, 26, 26, 448)  0           conv4_block7_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block7_1_conv (Conv2D)    (None, 26, 26, 128)  57344       conv4_block7_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block7_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block7_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block7_1_relu (Activation (None, 26, 26, 128)  0           conv4_block7_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block7_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block7_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block7_concat (Concatenat (None, 26, 26, 480)  0           conv4_block6_concat[0][0]        
                                                                 conv4_block7_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block8_0_bn (BatchNormali (None, 26, 26, 480)  1920        conv4_block7_concat[0][0]        
__________________________________________________________________________________________________
conv4_block8_0_relu (Activation (None, 26, 26, 480)  0           conv4_block8_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block8_1_conv (Conv2D)    (None, 26, 26, 128)  61440       conv4_block8_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block8_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block8_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block8_1_relu (Activation (None, 26, 26, 128)  0           conv4_block8_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block8_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block8_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block8_concat (Concatenat (None, 26, 26, 512)  0           conv4_block7_concat[0][0]        
                                                                 conv4_block8_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block9_0_bn (BatchNormali (None, 26, 26, 512)  2048        conv4_block8_concat[0][0]        
__________________________________________________________________________________________________
conv4_block9_0_relu (Activation (None, 26, 26, 512)  0           conv4_block9_0_bn[0][0]          
__________________________________________________________________________________________________
conv4_block9_1_conv (Conv2D)    (None, 26, 26, 128)  65536       conv4_block9_0_relu[0][0]        
__________________________________________________________________________________________________
conv4_block9_1_bn (BatchNormali (None, 26, 26, 128)  512         conv4_block9_1_conv[0][0]        
__________________________________________________________________________________________________
conv4_block9_1_relu (Activation (None, 26, 26, 128)  0           conv4_block9_1_bn[0][0]          
__________________________________________________________________________________________________
conv4_block9_2_conv (Conv2D)    (None, 26, 26, 32)   36864       conv4_block9_1_relu[0][0]        
__________________________________________________________________________________________________
conv4_block9_concat (Concatenat (None, 26, 26, 544)  0           conv4_block8_concat[0][0]        
                                                                 conv4_block9_2_conv[0][0]        
__________________________________________________________________________________________________
conv4_block10_0_bn (BatchNormal (None, 26, 26, 544)  2176        conv4_block9_concat[0][0]        
__________________________________________________________________________________________________
conv4_block10_0_relu (Activatio (None, 26, 26, 544)  0           conv4_block10_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block10_1_conv (Conv2D)   (None, 26, 26, 128)  69632       conv4_block10_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block10_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block10_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block10_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block10_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block10_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block10_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block10_concat (Concatena (None, 26, 26, 576)  0           conv4_block9_concat[0][0]        
                                                                 conv4_block10_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block11_0_bn (BatchNormal (None, 26, 26, 576)  2304        conv4_block10_concat[0][0]       
__________________________________________________________________________________________________
conv4_block11_0_relu (Activatio (None, 26, 26, 576)  0           conv4_block11_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block11_1_conv (Conv2D)   (None, 26, 26, 128)  73728       conv4_block11_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block11_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block11_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block11_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block11_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block11_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block11_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block11_concat (Concatena (None, 26, 26, 608)  0           conv4_block10_concat[0][0]       
                                                                 conv4_block11_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block12_0_bn (BatchNormal (None, 26, 26, 608)  2432        conv4_block11_concat[0][0]       
__________________________________________________________________________________________________
conv4_block12_0_relu (Activatio (None, 26, 26, 608)  0           conv4_block12_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block12_1_conv (Conv2D)   (None, 26, 26, 128)  77824       conv4_block12_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block12_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block12_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block12_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block12_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block12_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block12_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block12_concat (Concatena (None, 26, 26, 640)  0           conv4_block11_concat[0][0]       
                                                                 conv4_block12_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block13_0_bn (BatchNormal (None, 26, 26, 640)  2560        conv4_block12_concat[0][0]       
__________________________________________________________________________________________________
conv4_block13_0_relu (Activatio (None, 26, 26, 640)  0           conv4_block13_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block13_1_conv (Conv2D)   (None, 26, 26, 128)  81920       conv4_block13_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block13_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block13_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block13_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block13_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block13_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block13_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block13_concat (Concatena (None, 26, 26, 672)  0           conv4_block12_concat[0][0]       
                                                                 conv4_block13_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block14_0_bn (BatchNormal (None, 26, 26, 672)  2688        conv4_block13_concat[0][0]       
__________________________________________________________________________________________________
conv4_block14_0_relu (Activatio (None, 26, 26, 672)  0           conv4_block14_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block14_1_conv (Conv2D)   (None, 26, 26, 128)  86016       conv4_block14_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block14_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block14_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block14_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block14_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block14_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block14_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block14_concat (Concatena (None, 26, 26, 704)  0           conv4_block13_concat[0][0]       
                                                                 conv4_block14_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block15_0_bn (BatchNormal (None, 26, 26, 704)  2816        conv4_block14_concat[0][0]       
__________________________________________________________________________________________________
conv4_block15_0_relu (Activatio (None, 26, 26, 704)  0           conv4_block15_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block15_1_conv (Conv2D)   (None, 26, 26, 128)  90112       conv4_block15_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block15_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block15_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block15_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block15_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block15_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block15_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block15_concat (Concatena (None, 26, 26, 736)  0           conv4_block14_concat[0][0]       
                                                                 conv4_block15_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block16_0_bn (BatchNormal (None, 26, 26, 736)  2944        conv4_block15_concat[0][0]       
__________________________________________________________________________________________________
conv4_block16_0_relu (Activatio (None, 26, 26, 736)  0           conv4_block16_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block16_1_conv (Conv2D)   (None, 26, 26, 128)  94208       conv4_block16_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block16_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block16_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block16_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block16_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block16_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block16_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block16_concat (Concatena (None, 26, 26, 768)  0           conv4_block15_concat[0][0]       
                                                                 conv4_block16_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block17_0_bn (BatchNormal (None, 26, 26, 768)  3072        conv4_block16_concat[0][0]       
__________________________________________________________________________________________________
conv4_block17_0_relu (Activatio (None, 26, 26, 768)  0           conv4_block17_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block17_1_conv (Conv2D)   (None, 26, 26, 128)  98304       conv4_block17_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block17_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block17_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block17_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block17_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block17_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block17_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block17_concat (Concatena (None, 26, 26, 800)  0           conv4_block16_concat[0][0]       
                                                                 conv4_block17_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block18_0_bn (BatchNormal (None, 26, 26, 800)  3200        conv4_block17_concat[0][0]       
__________________________________________________________________________________________________
conv4_block18_0_relu (Activatio (None, 26, 26, 800)  0           conv4_block18_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block18_1_conv (Conv2D)   (None, 26, 26, 128)  102400      conv4_block18_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block18_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block18_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block18_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block18_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block18_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block18_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block18_concat (Concatena (None, 26, 26, 832)  0           conv4_block17_concat[0][0]       
                                                                 conv4_block18_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block19_0_bn (BatchNormal (None, 26, 26, 832)  3328        conv4_block18_concat[0][0]       
__________________________________________________________________________________________________
conv4_block19_0_relu (Activatio (None, 26, 26, 832)  0           conv4_block19_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block19_1_conv (Conv2D)   (None, 26, 26, 128)  106496      conv4_block19_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block19_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block19_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block19_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block19_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block19_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block19_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block19_concat (Concatena (None, 26, 26, 864)  0           conv4_block18_concat[0][0]       
                                                                 conv4_block19_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block20_0_bn (BatchNormal (None, 26, 26, 864)  3456        conv4_block19_concat[0][0]       
__________________________________________________________________________________________________
conv4_block20_0_relu (Activatio (None, 26, 26, 864)  0           conv4_block20_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block20_1_conv (Conv2D)   (None, 26, 26, 128)  110592      conv4_block20_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block20_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block20_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block20_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block20_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block20_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block20_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block20_concat (Concatena (None, 26, 26, 896)  0           conv4_block19_concat[0][0]       
                                                                 conv4_block20_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block21_0_bn (BatchNormal (None, 26, 26, 896)  3584        conv4_block20_concat[0][0]       
__________________________________________________________________________________________________
conv4_block21_0_relu (Activatio (None, 26, 26, 896)  0           conv4_block21_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block21_1_conv (Conv2D)   (None, 26, 26, 128)  114688      conv4_block21_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block21_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block21_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block21_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block21_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block21_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block21_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block21_concat (Concatena (None, 26, 26, 928)  0           conv4_block20_concat[0][0]       
                                                                 conv4_block21_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block22_0_bn (BatchNormal (None, 26, 26, 928)  3712        conv4_block21_concat[0][0]       
__________________________________________________________________________________________________
conv4_block22_0_relu (Activatio (None, 26, 26, 928)  0           conv4_block22_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block22_1_conv (Conv2D)   (None, 26, 26, 128)  118784      conv4_block22_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block22_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block22_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block22_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block22_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block22_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block22_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block22_concat (Concatena (None, 26, 26, 960)  0           conv4_block21_concat[0][0]       
                                                                 conv4_block22_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block23_0_bn (BatchNormal (None, 26, 26, 960)  3840        conv4_block22_concat[0][0]       
__________________________________________________________________________________________________
conv4_block23_0_relu (Activatio (None, 26, 26, 960)  0           conv4_block23_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block23_1_conv (Conv2D)   (None, 26, 26, 128)  122880      conv4_block23_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block23_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block23_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block23_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block23_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block23_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block23_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block23_concat (Concatena (None, 26, 26, 992)  0           conv4_block22_concat[0][0]       
                                                                 conv4_block23_2_conv[0][0]       
__________________________________________________________________________________________________
conv4_block24_0_bn (BatchNormal (None, 26, 26, 992)  3968        conv4_block23_concat[0][0]       
__________________________________________________________________________________________________
conv4_block24_0_relu (Activatio (None, 26, 26, 992)  0           conv4_block24_0_bn[0][0]         
__________________________________________________________________________________________________
conv4_block24_1_conv (Conv2D)   (None, 26, 26, 128)  126976      conv4_block24_0_relu[0][0]       
__________________________________________________________________________________________________
conv4_block24_1_bn (BatchNormal (None, 26, 26, 128)  512         conv4_block24_1_conv[0][0]       
__________________________________________________________________________________________________
conv4_block24_1_relu (Activatio (None, 26, 26, 128)  0           conv4_block24_1_bn[0][0]         
__________________________________________________________________________________________________
conv4_block24_2_conv (Conv2D)   (None, 26, 26, 32)   36864       conv4_block24_1_relu[0][0]       
__________________________________________________________________________________________________
conv4_block24_concat (Concatena (None, 26, 26, 1024) 0           conv4_block23_concat[0][0]       
                                                                 conv4_block24_2_conv[0][0]       
__________________________________________________________________________________________________
pool4_bn (BatchNormalization)   (None, 26, 26, 1024) 4096        conv4_block24_concat[0][0]       
__________________________________________________________________________________________________
pool4_relu (Activation)         (None, 26, 26, 1024) 0           pool4_bn[0][0]                   
__________________________________________________________________________________________________
pool4_conv (Conv2D)             (None, 26, 26, 512)  524288      pool4_relu[0][0]                 
__________________________________________________________________________________________________
pool4_pool (AveragePooling2D)   (None, 13, 13, 512)  0           pool4_conv[0][0]                 
__________________________________________________________________________________________________
conv5_block1_0_bn (BatchNormali (None, 13, 13, 512)  2048        pool4_pool[0][0]                 
__________________________________________________________________________________________________
conv5_block1_0_relu (Activation (None, 13, 13, 512)  0           conv5_block1_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_1_conv (Conv2D)    (None, 13, 13, 128)  65536       conv5_block1_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block1_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block1_1_relu (Activation (None, 13, 13, 128)  0           conv5_block1_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block1_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block1_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block1_concat (Concatenat (None, 13, 13, 544)  0           pool4_pool[0][0]                 
                                                                 conv5_block1_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_0_bn (BatchNormali (None, 13, 13, 544)  2176        conv5_block1_concat[0][0]        
__________________________________________________________________________________________________
conv5_block2_0_relu (Activation (None, 13, 13, 544)  0           conv5_block2_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_1_conv (Conv2D)    (None, 13, 13, 128)  69632       conv5_block2_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block2_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block2_1_relu (Activation (None, 13, 13, 128)  0           conv5_block2_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block2_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block2_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block2_concat (Concatenat (None, 13, 13, 576)  0           conv5_block1_concat[0][0]        
                                                                 conv5_block2_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_0_bn (BatchNormali (None, 13, 13, 576)  2304        conv5_block2_concat[0][0]        
__________________________________________________________________________________________________
conv5_block3_0_relu (Activation (None, 13, 13, 576)  0           conv5_block3_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_1_conv (Conv2D)    (None, 13, 13, 128)  73728       conv5_block3_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block3_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 13, 13, 128)  0           conv5_block3_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block3_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block3_concat (Concatenat (None, 13, 13, 608)  0           conv5_block2_concat[0][0]        
                                                                 conv5_block3_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block4_0_bn (BatchNormali (None, 13, 13, 608)  2432        conv5_block3_concat[0][0]        
__________________________________________________________________________________________________
conv5_block4_0_relu (Activation (None, 13, 13, 608)  0           conv5_block4_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block4_1_conv (Conv2D)    (None, 13, 13, 128)  77824       conv5_block4_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block4_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block4_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block4_1_relu (Activation (None, 13, 13, 128)  0           conv5_block4_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block4_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block4_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block4_concat (Concatenat (None, 13, 13, 640)  0           conv5_block3_concat[0][0]        
                                                                 conv5_block4_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block5_0_bn (BatchNormali (None, 13, 13, 640)  2560        conv5_block4_concat[0][0]        
__________________________________________________________________________________________________
conv5_block5_0_relu (Activation (None, 13, 13, 640)  0           conv5_block5_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block5_1_conv (Conv2D)    (None, 13, 13, 128)  81920       conv5_block5_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block5_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block5_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block5_1_relu (Activation (None, 13, 13, 128)  0           conv5_block5_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block5_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block5_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block5_concat (Concatenat (None, 13, 13, 672)  0           conv5_block4_concat[0][0]        
                                                                 conv5_block5_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block6_0_bn (BatchNormali (None, 13, 13, 672)  2688        conv5_block5_concat[0][0]        
__________________________________________________________________________________________________
conv5_block6_0_relu (Activation (None, 13, 13, 672)  0           conv5_block6_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block6_1_conv (Conv2D)    (None, 13, 13, 128)  86016       conv5_block6_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block6_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block6_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block6_1_relu (Activation (None, 13, 13, 128)  0           conv5_block6_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block6_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block6_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block6_concat (Concatenat (None, 13, 13, 704)  0           conv5_block5_concat[0][0]        
                                                                 conv5_block6_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block7_0_bn (BatchNormali (None, 13, 13, 704)  2816        conv5_block6_concat[0][0]        
__________________________________________________________________________________________________
conv5_block7_0_relu (Activation (None, 13, 13, 704)  0           conv5_block7_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block7_1_conv (Conv2D)    (None, 13, 13, 128)  90112       conv5_block7_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block7_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block7_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block7_1_relu (Activation (None, 13, 13, 128)  0           conv5_block7_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block7_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block7_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block7_concat (Concatenat (None, 13, 13, 736)  0           conv5_block6_concat[0][0]        
                                                                 conv5_block7_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block8_0_bn (BatchNormali (None, 13, 13, 736)  2944        conv5_block7_concat[0][0]        
__________________________________________________________________________________________________
conv5_block8_0_relu (Activation (None, 13, 13, 736)  0           conv5_block8_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block8_1_conv (Conv2D)    (None, 13, 13, 128)  94208       conv5_block8_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block8_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block8_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block8_1_relu (Activation (None, 13, 13, 128)  0           conv5_block8_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block8_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block8_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block8_concat (Concatenat (None, 13, 13, 768)  0           conv5_block7_concat[0][0]        
                                                                 conv5_block8_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block9_0_bn (BatchNormali (None, 13, 13, 768)  3072        conv5_block8_concat[0][0]        
__________________________________________________________________________________________________
conv5_block9_0_relu (Activation (None, 13, 13, 768)  0           conv5_block9_0_bn[0][0]          
__________________________________________________________________________________________________
conv5_block9_1_conv (Conv2D)    (None, 13, 13, 128)  98304       conv5_block9_0_relu[0][0]        
__________________________________________________________________________________________________
conv5_block9_1_bn (BatchNormali (None, 13, 13, 128)  512         conv5_block9_1_conv[0][0]        
__________________________________________________________________________________________________
conv5_block9_1_relu (Activation (None, 13, 13, 128)  0           conv5_block9_1_bn[0][0]          
__________________________________________________________________________________________________
conv5_block9_2_conv (Conv2D)    (None, 13, 13, 32)   36864       conv5_block9_1_relu[0][0]        
__________________________________________________________________________________________________
conv5_block9_concat (Concatenat (None, 13, 13, 800)  0           conv5_block8_concat[0][0]        
                                                                 conv5_block9_2_conv[0][0]        
__________________________________________________________________________________________________
conv5_block10_0_bn (BatchNormal (None, 13, 13, 800)  3200        conv5_block9_concat[0][0]        
__________________________________________________________________________________________________
conv5_block10_0_relu (Activatio (None, 13, 13, 800)  0           conv5_block10_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block10_1_conv (Conv2D)   (None, 13, 13, 128)  102400      conv5_block10_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block10_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block10_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block10_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block10_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block10_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block10_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block10_concat (Concatena (None, 13, 13, 832)  0           conv5_block9_concat[0][0]        
                                                                 conv5_block10_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block11_0_bn (BatchNormal (None, 13, 13, 832)  3328        conv5_block10_concat[0][0]       
__________________________________________________________________________________________________
conv5_block11_0_relu (Activatio (None, 13, 13, 832)  0           conv5_block11_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block11_1_conv (Conv2D)   (None, 13, 13, 128)  106496      conv5_block11_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block11_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block11_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block11_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block11_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block11_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block11_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block11_concat (Concatena (None, 13, 13, 864)  0           conv5_block10_concat[0][0]       
                                                                 conv5_block11_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block12_0_bn (BatchNormal (None, 13, 13, 864)  3456        conv5_block11_concat[0][0]       
__________________________________________________________________________________________________
conv5_block12_0_relu (Activatio (None, 13, 13, 864)  0           conv5_block12_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block12_1_conv (Conv2D)   (None, 13, 13, 128)  110592      conv5_block12_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block12_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block12_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block12_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block12_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block12_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block12_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block12_concat (Concatena (None, 13, 13, 896)  0           conv5_block11_concat[0][0]       
                                                                 conv5_block12_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block13_0_bn (BatchNormal (None, 13, 13, 896)  3584        conv5_block12_concat[0][0]       
__________________________________________________________________________________________________
conv5_block13_0_relu (Activatio (None, 13, 13, 896)  0           conv5_block13_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block13_1_conv (Conv2D)   (None, 13, 13, 128)  114688      conv5_block13_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block13_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block13_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block13_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block13_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block13_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block13_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block13_concat (Concatena (None, 13, 13, 928)  0           conv5_block12_concat[0][0]       
                                                                 conv5_block13_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block14_0_bn (BatchNormal (None, 13, 13, 928)  3712        conv5_block13_concat[0][0]       
__________________________________________________________________________________________________
conv5_block14_0_relu (Activatio (None, 13, 13, 928)  0           conv5_block14_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block14_1_conv (Conv2D)   (None, 13, 13, 128)  118784      conv5_block14_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block14_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block14_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block14_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block14_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block14_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block14_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block14_concat (Concatena (None, 13, 13, 960)  0           conv5_block13_concat[0][0]       
                                                                 conv5_block14_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block15_0_bn (BatchNormal (None, 13, 13, 960)  3840        conv5_block14_concat[0][0]       
__________________________________________________________________________________________________
conv5_block15_0_relu (Activatio (None, 13, 13, 960)  0           conv5_block15_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block15_1_conv (Conv2D)   (None, 13, 13, 128)  122880      conv5_block15_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block15_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block15_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block15_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block15_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block15_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block15_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block15_concat (Concatena (None, 13, 13, 992)  0           conv5_block14_concat[0][0]       
                                                                 conv5_block15_2_conv[0][0]       
__________________________________________________________________________________________________
conv5_block16_0_bn (BatchNormal (None, 13, 13, 992)  3968        conv5_block15_concat[0][0]       
__________________________________________________________________________________________________
conv5_block16_0_relu (Activatio (None, 13, 13, 992)  0           conv5_block16_0_bn[0][0]         
__________________________________________________________________________________________________
conv5_block16_1_conv (Conv2D)   (None, 13, 13, 128)  126976      conv5_block16_0_relu[0][0]       
__________________________________________________________________________________________________
conv5_block16_1_bn (BatchNormal (None, 13, 13, 128)  512         conv5_block16_1_conv[0][0]       
__________________________________________________________________________________________________
conv5_block16_1_relu (Activatio (None, 13, 13, 128)  0           conv5_block16_1_bn[0][0]         
__________________________________________________________________________________________________
conv5_block16_2_conv (Conv2D)   (None, 13, 13, 32)   36864       conv5_block16_1_relu[0][0]       
__________________________________________________________________________________________________
conv5_block16_concat (Concatena (None, 13, 13, 1024) 0           conv5_block15_concat[0][0]       
                                                                 conv5_block16_2_conv[0][0]       
__________________________________________________________________________________________________
bn (BatchNormalization)         (None, 13, 13, 1024) 4096        conv5_block16_concat[0][0]       
__________________________________________________________________________________________________
relu (Activation)               (None, 13, 13, 1024) 0           bn[0][0]                         
__________________________________________________________________________________________________
avg_pool (GlobalAveragePooling2 (None, 1024)         0           relu[0][0]                       
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 1)            1025        avg_pool[0][0]                   
==================================================================================================
Total params: 7,038,529
Trainable params: 6,937,025
Non-trainable params: 101,504
__________________________________________________________________________________________________

# setting up callbacks
checkpoint = ModelCheckpoint(filepath='classifier_weights.hdf5', monitor='val_accuracy',  verbose=0, save_best_only=True,save_weights_only=True, mode='auto')
log_dir="classifier_logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard= TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True,write_grads=True)
callback_list = [checkpoint,tensorboard]

WARNING:tensorflow:`write_grads` will be ignored in TensorFlow 2.0 for the `TensorBoard` Callback.

import warnings
warnings.filterwarnings("ignore")
# fit the model
history = model.fit(train_generator,
  validation_data=valid_generator,
  epochs=3,
  steps_per_epoch=len(train_generator),
  callbacks=callback_list)

Epoch 1/3
   1/4617 [..............................] - ETA: 3s - loss: 0.6653 - accuracy: 0.5000 - f1_m: 0.0000e+00WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.
Instructions for updating:
use `tf.profiler.experimental.stop` instead.
   2/4617 [..............................] - ETA: 15:44 - loss: 0.5988 - accuracy: 0.7500 - f1_m: 0.0000e+00WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0817s vs `on_train_batch_end` time: 0.3281s). Check your callbacks.
4617/4617 [==============================] - 1504s 326ms/step - loss: 0.4288 - accuracy: 0.8061 - f1_m: 0.2516 - val_loss: 0.3817 - val_accuracy: 0.8300 - val_f1_m: 0.3614
Epoch 2/3
4617/4617 [==============================] - 1451s 314ms/step - loss: 0.4022 - accuracy: 0.8177 - f1_m: 0.2970 - val_loss: 0.3846 - val_accuracy: 0.8270 - val_f1_m: 0.4200
Epoch 3/3
4617/4617 [==============================] - 1452s 315ms/step - loss: 0.3885 - accuracy: 0.8273 - f1_m: 0.3083 - val_loss: 0.3648 - val_accuracy: 0.8399 - val_f1_m: 0.3636

# loading model 
#model = tf.keras.models.load_model('models and weights/chexnet_model.hdf5',custom_objects={'f1_m':f1_m})
# loading weights
#model.load_weights('models and weights/classifier_weights.hdf5')

model.save_weights('chexnet_model_weights.hdf5')
# saving model
model.save('chexnet_model.hdf5')

plt.figure(figsize=(15,5))
plt.subplot(141)
plt.plot(history.epoch, history.history["loss"], label="Train loss")
plt.plot(history.epoch, history.history["val_loss"], label="Valid loss")
plt.legend()
plt.subplot(142)
plt.plot(history.epoch, history.history["accuracy"], label="Train accuracy")
plt.plot(history.epoch, history.history["val_accuracy"], label="Valid accuracy")
plt.legend()
plt.subplot(143)
plt.plot(history.epoch, history.history["f1_m"], label="Train f1")
plt.plot(history.epoch, history.history["val_f1_m"], label="Valid f1")
plt.legend()

<matplotlib.legend.Legend at 0x7f1b465fe5c0>

Approach & Summary:-¶

• We have defined the DenseNet model preloaded with Dense121 Weights.

• Input shape = 244,244,3

• Model loaded with the pre-trained chexnet weights available from the git-hub “brucechou1983_CheXNet_Keras_0.3.0_weights.h5”

• we used pretrained ChexNet architecture initialized with trained weights by removing top layers to make it binary classifier.

• We removed the last Dense layer with 14 classes and replaced with the Dense layer of 1 class.

• ChexNet provides a average F1 score which can be considered for further evaluation.

Retina Net Implementation for Pneumonia Detection¶

# Clone the git for RetinaNet implementation
!git clone "https://github.com/fizyr/keras-retinanet.git"

Cloning into 'keras-retinanet'...
remote: Enumerating objects: 6205, done.
remote: Total 6205 (delta 0), reused 0 (delta 0), pack-reused 6205
Receiving objects: 100% (6205/6205), 13.47 MiB | 11.33 MiB/s, done.
Resolving deltas: 100% (4199/4199), done.

# Install the pre-requisites and import required libraries
!pip install pydicom
import glob, pylab, pandas as pd
import numpy as np
import matplotlib.image as image
import pydicom as dcm
from skimage.transform import resize

Requirement already satisfied: pydicom in /usr/local/lib/python3.6/dist-packages (2.1.2)

%cd keras-retinanet

/content/keras-retinanet

# Install the required directory
!pip install .

Processing /content/keras-retinanet
Collecting keras-resnet==0.2.0
  Downloading https://files.pythonhosted.org/packages/76/d4/a35cbd07381139dda4db42c81b88c59254faac026109022727b45b31bcad/keras-resnet-0.2.0.tar.gz
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from keras-retinanet==1.0.0) (1.15.0)
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from keras-retinanet==1.0.0) (1.19.4)
Requirement already satisfied: cython in /usr/local/lib/python3.6/dist-packages (from keras-retinanet==1.0.0) (0.29.21)
Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from keras-retinanet==1.0.0) (7.0.0)
Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from keras-retinanet==1.0.0) (4.1.2.30)
Requirement already satisfied: progressbar2 in /usr/local/lib/python3.6/dist-packages (from keras-retinanet==1.0.0) (3.38.0)
Requirement already satisfied: keras>=2.2.4 in /usr/local/lib/python3.6/dist-packages (from keras-resnet==0.2.0->keras-retinanet==1.0.0) (2.4.3)
Requirement already satisfied: python-utils>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from progressbar2->keras-retinanet==1.0.0) (2.4.0)
Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-resnet==0.2.0->keras-retinanet==1.0.0) (1.4.1)
Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-resnet==0.2.0->keras-retinanet==1.0.0) (3.13)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras>=2.2.4->keras-resnet==0.2.0->keras-retinanet==1.0.0) (2.10.0)
Building wheels for collected packages: keras-retinanet, keras-resnet
  Building wheel for keras-retinanet (setup.py) ... done
  Created wheel for keras-retinanet: filename=keras_retinanet-1.0.0-cp36-cp36m-linux_x86_64.whl size=167026 sha256=2c690f622d8947639696813941aa517336e39d8bb0e5147e710eb7ecd2befd58
  Stored in directory: /root/.cache/pip/wheels/b2/9f/57/cb0305f6f5a41fc3c11ad67b8cedfbe9127775b563337827ba
  Building wheel for keras-resnet (setup.py) ... done
  Created wheel for keras-resnet: filename=keras_resnet-0.2.0-py2.py3-none-any.whl size=20486 sha256=971ae2e2af650c142c6b4536a2d14df6c46fd16d216ead51cda6336da93a159a
  Stored in directory: /root/.cache/pip/wheels/5f/09/a5/497a30fd9ad9964e98a1254d1e164bcd1b8a5eda36197ecb3c
Successfully built keras-retinanet keras-resnet
Installing collected packages: keras-resnet, keras-retinanet
Successfully installed keras-resnet-0.2.0 keras-retinanet-1.0.0

# Compile the script using the setup file
!python setup.py build_ext --inplace

running build_ext
cythoning keras_retinanet/utils/compute_overlap.pyx to keras_retinanet/utils/compute_overlap.c
/usr/local/lib/python3.6/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/keras-retinanet/keras_retinanet/utils/compute_overlap.pyx
  tree = Parsing.p_module(s, pxd, full_module_name)
building 'keras_retinanet.utils.compute_overlap' extension
creating build
creating build/temp.linux-x86_64-3.6
creating build/temp.linux-x86_64-3.6/keras_retinanet
creating build/temp.linux-x86_64-3.6/keras_retinanet/utils
x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/include/python3.6m -I/usr/local/lib/python3.6/dist-packages/numpy/core/include -c keras_retinanet/utils/compute_overlap.c -o build/temp.linux-x86_64-3.6/keras_retinanet/utils/compute_overlap.o
In file included from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1822:0,
                 from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,
                 from /usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/arrayobject.h:4,
                 from keras_retinanet/utils/compute_overlap.c:610:
/usr/local/lib/python3.6/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning "Using deprecated NumPy API, disable it with " "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-Wcpp]
 #warning "Using deprecated NumPy API, disable it with " \
  ^~~~~~~
creating build/lib.linux-x86_64-3.6
creating build/lib.linux-x86_64-3.6/keras_retinanet
creating build/lib.linux-x86_64-3.6/keras_retinanet/utils
x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/keras_retinanet/utils/compute_overlap.o -o build/lib.linux-x86_64-3.6/keras_retinanet/utils/compute_overlap.cpython-36m-x86_64-linux-gnu.so
copying build/lib.linux-x86_64-3.6/keras_retinanet/utils/compute_overlap.cpython-36m-x86_64-linux-gnu.so -> keras_retinanet/utils

# Filter records with pnuemonia cases
detailed_class_df=train_data[train_data['Target']==1]

detailed_class_df.head()

detailed_class_df.shape

(9555, 10)

# Group images with different bounding boxes
def parse_data(df):
    extract_box = lambda row: [row['x'], row['y'], row['width'], row['height']]
    parsed = {}
    for n, row in df.iterrows():
        
        pid = row['patientId']
        if pid not in parsed:
            parsed[pid] = {
                'boxes': []}
        parsed[pid]['boxes'].append(extract_box(row))

    return parsed

# Convert dcm file to JPEG for usage in RetinaNet
def converttoJpeg(patientId):
    dicom_file = dcm.read_file(inp_path+patientId+'.dcm')
    dicom_array = dicom_file.pixel_array
    image_array = resize(dicom_array, (512, 512), mode= 'constant', anti_aliasing=True)
    image.imsave(out_path+patientId+".jpg", dicom_array)

# Get the first 4000 records for training
train_dict=parse_data(detailed_class_df.head(4000))

len(train_dict)

2519

#set path to the file location
out_path = '/content/drive/My Drive/Colab Notebooks/capstone/rsna-pneumonia-detection-challenge/JPEG/'
inp_path = '/content/drive/My Drive/Colab Notebooks/capstone/rsna-pneumonia-detection-challenge/stage_2_train_images/'

# Create annotation file for the images of the format image_path,x1,y1,x2,y3,'pnenumonia'
# Create class label file of the format 'pneumonia',0
for patient_id in train_dict.keys():
    box =train_dict[patient_id]['boxes']
    #converttoJpeg(patient_id)
    path = out_path+patient_id+".jpg"
    for j in range(len(box)):
      x1 = int(box[j][0])             #Upper lef 
      x2 = int(box[j][0] + box[j][2])
      y1 = int(box[j][1])
      y2 = int(box[j][1] + box[j][3]) #Upper left y1 + height  
      towrite = str(path+ "," + str(x1) + ","+ str(y1)+","+ str(x2)+","+ str(y2)+",Pneumonia")
      with open("annotate.csv", "a") as wr:
            wr.write(towrite)
            wr.write('\n')
      with open("class.csv", "w") as wr:
            towrite = str("Pneumonia,0")
            wr.write(towrite)

# Load pre-trained model weights
import urllib
#PRETRAINED_MODEL = './snapshots/_pretrained_model.h5'
PRETRAINED_MODEL = './snapshots/resnet50_csv_10.h5'
URL_MODEL = 'https://github.com/fizyr/keras-retinanet/releases/download/0.5.1/resnet50_coco_best_v2.1.0.h5'
urllib.request.urlretrieve(URL_MODEL, PRETRAINED_MODEL)

('./snapshots/resnet50_csv_10.h5', <http.client.HTTPMessage at 0x7f3583e326d8>)

!pip install keras==2.4.0

Collecting keras==2.4.0
  Downloading https://files.pythonhosted.org/packages/b6/19/9d8f1c86c09d05369da39b03d011cd689edef86c0e6b2777dbcedc49dfc6/Keras-2.4.0-py2.py3-none-any.whl (170kB)
     |████████████████████████████████| 174kB 12.3MB/s 
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Installing collected packages: keras
  Found existing installation: Keras 2.4.3
    Uninstalling Keras-2.4.3:
      Successfully uninstalled Keras-2.4.3
Successfully installed keras-2.4.0

!pwd

/content/keras-retinanet

!keras_retinanet/bin/train.py --freeze-backbone --random-transform --weights {PRETRAINED_MODEL} --batch-size 32 --steps 79 --lr 1e-4 --epochs 50 csv annotate.csv class.csv

2020-12-16 20:13:44.223213: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Using TensorFlow backend.
Creating model, this may take a second...
2020-12-16 20:32:17.748201: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-12-16 20:32:17.794191: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:17.794809: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-12-16 20:32:17.794852: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-12-16 20:32:18.026449: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-12-16 20:32:18.153488: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-12-16 20:32:18.178399: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-12-16 20:32:18.408412: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-12-16 20:32:18.434547: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-12-16 20:32:18.840644: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-12-16 20:32:18.840826: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:18.841497: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:18.842074: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-12-16 20:32:18.876531: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2200000000 Hz
2020-12-16 20:32:18.876847: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d24a00 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-12-16 20:32:18.876883: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-12-16 20:32:19.008778: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:19.009545: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d259c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-12-16 20:32:19.009606: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2020-12-16 20:32:19.010543: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:19.011144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-12-16 20:32:19.011202: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-12-16 20:32:19.011248: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-12-16 20:32:19.011273: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-12-16 20:32:19.011293: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-12-16 20:32:19.011313: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-12-16 20:32:19.011334: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-12-16 20:32:19.011356: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-12-16 20:32:19.011426: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:19.011995: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:19.012492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-12-16 20:32:19.018269: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-12-16 20:32:21.059853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-12-16 20:32:21.059909: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
2020-12-16 20:32:21.059920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
2020-12-16 20:32:21.065876: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:21.066523: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-16 20:32:21.067109: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-12-16 20:32:21.067148: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14967 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
WARNING:tensorflow:Skipping loading of weights for layer classification_submodel due to mismatch in shape ((3, 3, 256, 9) vs (3, 3, 256, 720)).
WARNING:tensorflow:Skipping loading of weights for layer classification_submodel due to mismatch in shape ((9,) vs (720,)).
Model: "retinanet"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(None, None, None,  0                                            
__________________________________________________________________________________________________
conv1 (Conv2D)                  (None, None, None, 6 9408        input_1[0][0]                    
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization)   (None, None, None, 6 256         conv1[0][0]                      
__________________________________________________________________________________________________
conv1_relu (Activation)         (None, None, None, 6 0           bn_conv1[0][0]                   
__________________________________________________________________________________________________
pool1 (MaxPooling2D)            (None, None, None, 6 0           conv1_relu[0][0]                 
__________________________________________________________________________________________________
res2a_branch2a (Conv2D)         (None, None, None, 6 4096        pool1[0][0]                      
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, None, None, 6 256         res2a_branch2a[0][0]             
__________________________________________________________________________________________________
res2a_branch2a_relu (Activation (None, None, None, 6 0           bn2a_branch2a[0][0]              
__________________________________________________________________________________________________
padding2a_branch2b (ZeroPadding (None, None, None, 6 0           res2a_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res2a_branch2b (Conv2D)         (None, None, None, 6 36864       padding2a_branch2b[0][0]         
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, None, None, 6 256         res2a_branch2b[0][0]             
__________________________________________________________________________________________________
res2a_branch2b_relu (Activation (None, None, None, 6 0           bn2a_branch2b[0][0]              
__________________________________________________________________________________________________
res2a_branch2c (Conv2D)         (None, None, None, 2 16384       res2a_branch2b_relu[0][0]        
__________________________________________________________________________________________________
res2a_branch1 (Conv2D)          (None, None, None, 2 16384       pool1[0][0]                      
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, None, None, 2 1024        res2a_branch2c[0][0]             
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, None, None, 2 1024        res2a_branch1[0][0]              
__________________________________________________________________________________________________
res2a (Add)                     (None, None, None, 2 0           bn2a_branch2c[0][0]              
                                                                 bn2a_branch1[0][0]               
__________________________________________________________________________________________________
res2a_relu (Activation)         (None, None, None, 2 0           res2a[0][0]                      
__________________________________________________________________________________________________
res2b_branch2a (Conv2D)         (None, None, None, 6 16384       res2a_relu[0][0]                 
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, None, None, 6 256         res2b_branch2a[0][0]             
__________________________________________________________________________________________________
res2b_branch2a_relu (Activation (None, None, None, 6 0           bn2b_branch2a[0][0]              
__________________________________________________________________________________________________
padding2b_branch2b (ZeroPadding (None, None, None, 6 0           res2b_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res2b_branch2b (Conv2D)         (None, None, None, 6 36864       padding2b_branch2b[0][0]         
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, None, None, 6 256         res2b_branch2b[0][0]             
__________________________________________________________________________________________________
res2b_branch2b_relu (Activation (None, None, None, 6 0           bn2b_branch2b[0][0]              
__________________________________________________________________________________________________
res2b_branch2c (Conv2D)         (None, None, None, 2 16384       res2b_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, None, None, 2 1024        res2b_branch2c[0][0]             
__________________________________________________________________________________________________
res2b (Add)                     (None, None, None, 2 0           bn2b_branch2c[0][0]              
                                                                 res2a_relu[0][0]                 
__________________________________________________________________________________________________
res2b_relu (Activation)         (None, None, None, 2 0           res2b[0][0]                      
__________________________________________________________________________________________________
res2c_branch2a (Conv2D)         (None, None, None, 6 16384       res2b_relu[0][0]                 
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, None, None, 6 256         res2c_branch2a[0][0]             
__________________________________________________________________________________________________
res2c_branch2a_relu (Activation (None, None, None, 6 0           bn2c_branch2a[0][0]              
__________________________________________________________________________________________________
padding2c_branch2b (ZeroPadding (None, None, None, 6 0           res2c_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res2c_branch2b (Conv2D)         (None, None, None, 6 36864       padding2c_branch2b[0][0]         
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, None, None, 6 256         res2c_branch2b[0][0]             
__________________________________________________________________________________________________
res2c_branch2b_relu (Activation (None, None, None, 6 0           bn2c_branch2b[0][0]              
__________________________________________________________________________________________________
res2c_branch2c (Conv2D)         (None, None, None, 2 16384       res2c_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, None, None, 2 1024        res2c_branch2c[0][0]             
__________________________________________________________________________________________________
res2c (Add)                     (None, None, None, 2 0           bn2c_branch2c[0][0]              
                                                                 res2b_relu[0][0]                 
__________________________________________________________________________________________________
res2c_relu (Activation)         (None, None, None, 2 0           res2c[0][0]                      
__________________________________________________________________________________________________
res3a_branch2a (Conv2D)         (None, None, None, 1 32768       res2c_relu[0][0]                 
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, None, None, 1 512         res3a_branch2a[0][0]             
__________________________________________________________________________________________________
res3a_branch2a_relu (Activation (None, None, None, 1 0           bn3a_branch2a[0][0]              
__________________________________________________________________________________________________
padding3a_branch2b (ZeroPadding (None, None, None, 1 0           res3a_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res3a_branch2b (Conv2D)         (None, None, None, 1 147456      padding3a_branch2b[0][0]         
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, None, None, 1 512         res3a_branch2b[0][0]             
__________________________________________________________________________________________________
res3a_branch2b_relu (Activation (None, None, None, 1 0           bn3a_branch2b[0][0]              
__________________________________________________________________________________________________
res3a_branch2c (Conv2D)         (None, None, None, 5 65536       res3a_branch2b_relu[0][0]        
__________________________________________________________________________________________________
res3a_branch1 (Conv2D)          (None, None, None, 5 131072      res2c_relu[0][0]                 
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, None, None, 5 2048        res3a_branch2c[0][0]             
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, None, None, 5 2048        res3a_branch1[0][0]              
__________________________________________________________________________________________________
res3a (Add)                     (None, None, None, 5 0           bn3a_branch2c[0][0]              
                                                                 bn3a_branch1[0][0]               
__________________________________________________________________________________________________
res3a_relu (Activation)         (None, None, None, 5 0           res3a[0][0]                      
__________________________________________________________________________________________________
res3b_branch2a (Conv2D)         (None, None, None, 1 65536       res3a_relu[0][0]                 
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, None, None, 1 512         res3b_branch2a[0][0]             
__________________________________________________________________________________________________
res3b_branch2a_relu (Activation (None, None, None, 1 0           bn3b_branch2a[0][0]              
__________________________________________________________________________________________________
padding3b_branch2b (ZeroPadding (None, None, None, 1 0           res3b_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res3b_branch2b (Conv2D)         (None, None, None, 1 147456      padding3b_branch2b[0][0]         
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, None, None, 1 512         res3b_branch2b[0][0]             
__________________________________________________________________________________________________
res3b_branch2b_relu (Activation (None, None, None, 1 0           bn3b_branch2b[0][0]              
__________________________________________________________________________________________________
res3b_branch2c (Conv2D)         (None, None, None, 5 65536       res3b_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, None, None, 5 2048        res3b_branch2c[0][0]             
__________________________________________________________________________________________________
res3b (Add)                     (None, None, None, 5 0           bn3b_branch2c[0][0]              
                                                                 res3a_relu[0][0]                 
__________________________________________________________________________________________________
res3b_relu (Activation)         (None, None, None, 5 0           res3b[0][0]                      
__________________________________________________________________________________________________
res3c_branch2a (Conv2D)         (None, None, None, 1 65536       res3b_relu[0][0]                 
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, None, None, 1 512         res3c_branch2a[0][0]             
__________________________________________________________________________________________________
res3c_branch2a_relu (Activation (None, None, None, 1 0           bn3c_branch2a[0][0]              
__________________________________________________________________________________________________
padding3c_branch2b (ZeroPadding (None, None, None, 1 0           res3c_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res3c_branch2b (Conv2D)         (None, None, None, 1 147456      padding3c_branch2b[0][0]         
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, None, None, 1 512         res3c_branch2b[0][0]             
__________________________________________________________________________________________________
res3c_branch2b_relu (Activation (None, None, None, 1 0           bn3c_branch2b[0][0]              
__________________________________________________________________________________________________
res3c_branch2c (Conv2D)         (None, None, None, 5 65536       res3c_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, None, None, 5 2048        res3c_branch2c[0][0]             
__________________________________________________________________________________________________
res3c (Add)                     (None, None, None, 5 0           bn3c_branch2c[0][0]              
                                                                 res3b_relu[0][0]                 
__________________________________________________________________________________________________
res3c_relu (Activation)         (None, None, None, 5 0           res3c[0][0]                      
__________________________________________________________________________________________________
res3d_branch2a (Conv2D)         (None, None, None, 1 65536       res3c_relu[0][0]                 
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, None, None, 1 512         res3d_branch2a[0][0]             
__________________________________________________________________________________________________
res3d_branch2a_relu (Activation (None, None, None, 1 0           bn3d_branch2a[0][0]              
__________________________________________________________________________________________________
padding3d_branch2b (ZeroPadding (None, None, None, 1 0           res3d_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res3d_branch2b (Conv2D)         (None, None, None, 1 147456      padding3d_branch2b[0][0]         
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, None, None, 1 512         res3d_branch2b[0][0]             
__________________________________________________________________________________________________
res3d_branch2b_relu (Activation (None, None, None, 1 0           bn3d_branch2b[0][0]              
__________________________________________________________________________________________________
res3d_branch2c (Conv2D)         (None, None, None, 5 65536       res3d_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, None, None, 5 2048        res3d_branch2c[0][0]             
__________________________________________________________________________________________________
res3d (Add)                     (None, None, None, 5 0           bn3d_branch2c[0][0]              
                                                                 res3c_relu[0][0]                 
__________________________________________________________________________________________________
res3d_relu (Activation)         (None, None, None, 5 0           res3d[0][0]                      
__________________________________________________________________________________________________
res4a_branch2a (Conv2D)         (None, None, None, 2 131072      res3d_relu[0][0]                 
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, None, None, 2 1024        res4a_branch2a[0][0]             
__________________________________________________________________________________________________
res4a_branch2a_relu (Activation (None, None, None, 2 0           bn4a_branch2a[0][0]              
__________________________________________________________________________________________________
padding4a_branch2b (ZeroPadding (None, None, None, 2 0           res4a_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res4a_branch2b (Conv2D)         (None, None, None, 2 589824      padding4a_branch2b[0][0]         
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, None, None, 2 1024        res4a_branch2b[0][0]             
__________________________________________________________________________________________________
res4a_branch2b_relu (Activation (None, None, None, 2 0           bn4a_branch2b[0][0]              
__________________________________________________________________________________________________
res4a_branch2c (Conv2D)         (None, None, None, 1 262144      res4a_branch2b_relu[0][0]        
__________________________________________________________________________________________________
res4a_branch1 (Conv2D)          (None, None, None, 1 524288      res3d_relu[0][0]                 
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, None, None, 1 4096        res4a_branch2c[0][0]             
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, None, None, 1 4096        res4a_branch1[0][0]              
__________________________________________________________________________________________________
res4a (Add)                     (None, None, None, 1 0           bn4a_branch2c[0][0]              
                                                                 bn4a_branch1[0][0]               
__________________________________________________________________________________________________
res4a_relu (Activation)         (None, None, None, 1 0           res4a[0][0]                      
__________________________________________________________________________________________________
res4b_branch2a (Conv2D)         (None, None, None, 2 262144      res4a_relu[0][0]                 
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, None, None, 2 1024        res4b_branch2a[0][0]             
__________________________________________________________________________________________________
res4b_branch2a_relu (Activation (None, None, None, 2 0           bn4b_branch2a[0][0]              
__________________________________________________________________________________________________
padding4b_branch2b (ZeroPadding (None, None, None, 2 0           res4b_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res4b_branch2b (Conv2D)         (None, None, None, 2 589824      padding4b_branch2b[0][0]         
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, None, None, 2 1024        res4b_branch2b[0][0]             
__________________________________________________________________________________________________
res4b_branch2b_relu (Activation (None, None, None, 2 0           bn4b_branch2b[0][0]              
__________________________________________________________________________________________________
res4b_branch2c (Conv2D)         (None, None, None, 1 262144      res4b_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, None, None, 1 4096        res4b_branch2c[0][0]             
__________________________________________________________________________________________________
res4b (Add)                     (None, None, None, 1 0           bn4b_branch2c[0][0]              
                                                                 res4a_relu[0][0]                 
__________________________________________________________________________________________________
res4b_relu (Activation)         (None, None, None, 1 0           res4b[0][0]                      
__________________________________________________________________________________________________
res4c_branch2a (Conv2D)         (None, None, None, 2 262144      res4b_relu[0][0]                 
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, None, None, 2 1024        res4c_branch2a[0][0]             
__________________________________________________________________________________________________
res4c_branch2a_relu (Activation (None, None, None, 2 0           bn4c_branch2a[0][0]              
__________________________________________________________________________________________________
padding4c_branch2b (ZeroPadding (None, None, None, 2 0           res4c_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res4c_branch2b (Conv2D)         (None, None, None, 2 589824      padding4c_branch2b[0][0]         
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, None, None, 2 1024        res4c_branch2b[0][0]             
__________________________________________________________________________________________________
res4c_branch2b_relu (Activation (None, None, None, 2 0           bn4c_branch2b[0][0]              
__________________________________________________________________________________________________
res4c_branch2c (Conv2D)         (None, None, None, 1 262144      res4c_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, None, None, 1 4096        res4c_branch2c[0][0]             
__________________________________________________________________________________________________
res4c (Add)                     (None, None, None, 1 0           bn4c_branch2c[0][0]              
                                                                 res4b_relu[0][0]                 
__________________________________________________________________________________________________
res4c_relu (Activation)         (None, None, None, 1 0           res4c[0][0]                      
__________________________________________________________________________________________________
res4d_branch2a (Conv2D)         (None, None, None, 2 262144      res4c_relu[0][0]                 
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, None, None, 2 1024        res4d_branch2a[0][0]             
__________________________________________________________________________________________________
res4d_branch2a_relu (Activation (None, None, None, 2 0           bn4d_branch2a[0][0]              
__________________________________________________________________________________________________
padding4d_branch2b (ZeroPadding (None, None, None, 2 0           res4d_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res4d_branch2b (Conv2D)         (None, None, None, 2 589824      padding4d_branch2b[0][0]         
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, None, None, 2 1024        res4d_branch2b[0][0]             
__________________________________________________________________________________________________
res4d_branch2b_relu (Activation (None, None, None, 2 0           bn4d_branch2b[0][0]              
__________________________________________________________________________________________________
res4d_branch2c (Conv2D)         (None, None, None, 1 262144      res4d_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, None, None, 1 4096        res4d_branch2c[0][0]             
__________________________________________________________________________________________________
res4d (Add)                     (None, None, None, 1 0           bn4d_branch2c[0][0]              
                                                                 res4c_relu[0][0]                 
__________________________________________________________________________________________________
res4d_relu (Activation)         (None, None, None, 1 0           res4d[0][0]                      
__________________________________________________________________________________________________
res4e_branch2a (Conv2D)         (None, None, None, 2 262144      res4d_relu[0][0]                 
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, None, None, 2 1024        res4e_branch2a[0][0]             
__________________________________________________________________________________________________
res4e_branch2a_relu (Activation (None, None, None, 2 0           bn4e_branch2a[0][0]              
__________________________________________________________________________________________________
padding4e_branch2b (ZeroPadding (None, None, None, 2 0           res4e_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res4e_branch2b (Conv2D)         (None, None, None, 2 589824      padding4e_branch2b[0][0]         
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, None, None, 2 1024        res4e_branch2b[0][0]             
__________________________________________________________________________________________________
res4e_branch2b_relu (Activation (None, None, None, 2 0           bn4e_branch2b[0][0]              
__________________________________________________________________________________________________
res4e_branch2c (Conv2D)         (None, None, None, 1 262144      res4e_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizati (None, None, None, 1 4096        res4e_branch2c[0][0]             
__________________________________________________________________________________________________
res4e (Add)                     (None, None, None, 1 0           bn4e_branch2c[0][0]              
                                                                 res4d_relu[0][0]                 
__________________________________________________________________________________________________
res4e_relu (Activation)         (None, None, None, 1 0           res4e[0][0]                      
__________________________________________________________________________________________________
res4f_branch2a (Conv2D)         (None, None, None, 2 262144      res4e_relu[0][0]                 
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, None, None, 2 1024        res4f_branch2a[0][0]             
__________________________________________________________________________________________________
res4f_branch2a_relu (Activation (None, None, None, 2 0           bn4f_branch2a[0][0]              
__________________________________________________________________________________________________
padding4f_branch2b (ZeroPadding (None, None, None, 2 0           res4f_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res4f_branch2b (Conv2D)         (None, None, None, 2 589824      padding4f_branch2b[0][0]         
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, None, None, 2 1024        res4f_branch2b[0][0]             
__________________________________________________________________________________________________
res4f_branch2b_relu (Activation (None, None, None, 2 0           bn4f_branch2b[0][0]              
__________________________________________________________________________________________________
res4f_branch2c (Conv2D)         (None, None, None, 1 262144      res4f_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, None, None, 1 4096        res4f_branch2c[0][0]             
__________________________________________________________________________________________________
res4f (Add)                     (None, None, None, 1 0           bn4f_branch2c[0][0]              
                                                                 res4e_relu[0][0]                 
__________________________________________________________________________________________________
res4f_relu (Activation)         (None, None, None, 1 0           res4f[0][0]                      
__________________________________________________________________________________________________
res5a_branch2a (Conv2D)         (None, None, None, 5 524288      res4f_relu[0][0]                 
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, None, None, 5 2048        res5a_branch2a[0][0]             
__________________________________________________________________________________________________
res5a_branch2a_relu (Activation (None, None, None, 5 0           bn5a_branch2a[0][0]              
__________________________________________________________________________________________________
padding5a_branch2b (ZeroPadding (None, None, None, 5 0           res5a_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res5a_branch2b (Conv2D)         (None, None, None, 5 2359296     padding5a_branch2b[0][0]         
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, None, None, 5 2048        res5a_branch2b[0][0]             
__________________________________________________________________________________________________
res5a_branch2b_relu (Activation (None, None, None, 5 0           bn5a_branch2b[0][0]              
__________________________________________________________________________________________________
res5a_branch2c (Conv2D)         (None, None, None, 2 1048576     res5a_branch2b_relu[0][0]        
__________________________________________________________________________________________________
res5a_branch1 (Conv2D)          (None, None, None, 2 2097152     res4f_relu[0][0]                 
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, None, None, 2 8192        res5a_branch2c[0][0]             
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, None, None, 2 8192        res5a_branch1[0][0]              
__________________________________________________________________________________________________
res5a (Add)                     (None, None, None, 2 0           bn5a_branch2c[0][0]              
                                                                 bn5a_branch1[0][0]               
__________________________________________________________________________________________________
res5a_relu (Activation)         (None, None, None, 2 0           res5a[0][0]                      
__________________________________________________________________________________________________
res5b_branch2a (Conv2D)         (None, None, None, 5 1048576     res5a_relu[0][0]                 
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, None, None, 5 2048        res5b_branch2a[0][0]             
__________________________________________________________________________________________________
res5b_branch2a_relu (Activation (None, None, None, 5 0           bn5b_branch2a[0][0]              
__________________________________________________________________________________________________
padding5b_branch2b (ZeroPadding (None, None, None, 5 0           res5b_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res5b_branch2b (Conv2D)         (None, None, None, 5 2359296     padding5b_branch2b[0][0]         
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, None, None, 5 2048        res5b_branch2b[0][0]             
__________________________________________________________________________________________________
res5b_branch2b_relu (Activation (None, None, None, 5 0           bn5b_branch2b[0][0]              
__________________________________________________________________________________________________
res5b_branch2c (Conv2D)         (None, None, None, 2 1048576     res5b_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, None, None, 2 8192        res5b_branch2c[0][0]             
__________________________________________________________________________________________________
res5b (Add)                     (None, None, None, 2 0           bn5b_branch2c[0][0]              
                                                                 res5a_relu[0][0]                 
__________________________________________________________________________________________________
res5b_relu (Activation)         (None, None, None, 2 0           res5b[0][0]                      
__________________________________________________________________________________________________
res5c_branch2a (Conv2D)         (None, None, None, 5 1048576     res5b_relu[0][0]                 
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, None, None, 5 2048        res5c_branch2a[0][0]             
__________________________________________________________________________________________________
res5c_branch2a_relu (Activation (None, None, None, 5 0           bn5c_branch2a[0][0]              
__________________________________________________________________________________________________
padding5c_branch2b (ZeroPadding (None, None, None, 5 0           res5c_branch2a_relu[0][0]        
__________________________________________________________________________________________________
res5c_branch2b (Conv2D)         (None, None, None, 5 2359296     padding5c_branch2b[0][0]         
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, None, None, 5 2048        res5c_branch2b[0][0]             
__________________________________________________________________________________________________
res5c_branch2b_relu (Activation (None, None, None, 5 0           bn5c_branch2b[0][0]              
__________________________________________________________________________________________________
res5c_branch2c (Conv2D)         (None, None, None, 2 1048576     res5c_branch2b_relu[0][0]        
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, None, None, 2 8192        res5c_branch2c[0][0]             
__________________________________________________________________________________________________
res5c (Add)                     (None, None, None, 2 0           bn5c_branch2c[0][0]              
                                                                 res5b_relu[0][0]                 
__________________________________________________________________________________________________
res5c_relu (Activation)         (None, None, None, 2 0           res5c[0][0]                      
__________________________________________________________________________________________________
C5_reduced (Conv2D)             (None, None, None, 2 524544      res5c_relu[0][0]                 
__________________________________________________________________________________________________
P5_upsampled (UpsampleLike)     (None, None, None, 2 0           C5_reduced[0][0]                 
                                                                 res4f_relu[0][0]                 
__________________________________________________________________________________________________
C4_reduced (Conv2D)             (None, None, None, 2 262400      res4f_relu[0][0]                 
__________________________________________________________________________________________________
P4_merged (Add)                 (None, None, None, 2 0           P5_upsampled[0][0]               
                                                                 C4_reduced[0][0]                 
__________________________________________________________________________________________________
P4_upsampled (UpsampleLike)     (None, None, None, 2 0           P4_merged[0][0]                  
                                                                 res3d_relu[0][0]                 
__________________________________________________________________________________________________
C3_reduced (Conv2D)             (None, None, None, 2 131328      res3d_relu[0][0]                 
__________________________________________________________________________________________________
P6 (Conv2D)                     (None, None, None, 2 4718848     res5c_relu[0][0]                 
__________________________________________________________________________________________________
P3_merged (Add)                 (None, None, None, 2 0           P4_upsampled[0][0]               
                                                                 C3_reduced[0][0]                 
__________________________________________________________________________________________________
C6_relu (Activation)            (None, None, None, 2 0           P6[0][0]                         
__________________________________________________________________________________________________
P3 (Conv2D)                     (None, None, None, 2 590080      P3_merged[0][0]                  
__________________________________________________________________________________________________
P4 (Conv2D)                     (None, None, None, 2 590080      P4_merged[0][0]                  
__________________________________________________________________________________________________
P5 (Conv2D)                     (None, None, None, 2 590080      C5_reduced[0][0]                 
__________________________________________________________________________________________________
P7 (Conv2D)                     (None, None, None, 2 590080      C6_relu[0][0]                    
__________________________________________________________________________________________________
regression_submodel (Functional (None, None, 4)      2443300     P3[0][0]                         
                                                                 P4[0][0]                         
                                                                 P5[0][0]                         
                                                                 P6[0][0]                         
                                                                 P7[0][0]                         
__________________________________________________________________________________________________
classification_submodel (Functi (None, None, 1)      2381065     P3[0][0]                         
                                                                 P4[0][0]                         
                                                                 P5[0][0]                         
                                                                 P6[0][0]                         
                                                                 P7[0][0]                         
__________________________________________________________________________________________________
regression (Concatenate)        (None, None, 4)      0           regression_submodel[0][0]        
                                                                 regression_submodel[1][0]        
                                                                 regression_submodel[2][0]        
                                                                 regression_submodel[3][0]        
                                                                 regression_submodel[4][0]        
__________________________________________________________________________________________________
classification (Concatenate)    (None, None, 1)      0           classification_submodel[0][0]    
                                                                 classification_submodel[1][0]    
                                                                 classification_submodel[2][0]    
                                                                 classification_submodel[3][0]    
                                                                 classification_submodel[4][0]    
==================================================================================================
Total params: 36,382,957
Trainable params: 12,821,805
Non-trainable params: 23,561,152
__________________________________________________________________________________________________
None
WARNING:tensorflow:From keras_retinanet/bin/train.py:548: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
Epoch 1/50
2020-12-16 20:32:38.563396: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-12-16 20:32:41.546428: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
79/79 [==============================] - ETA: 0s - loss: 2.8190 - regression_loss: 2.2355 - classification_loss: 0.5836
Epoch 00001: saving model to ./snapshots/resnet50_csv_01.h5
79/79 [==============================] - 357s 5s/step - loss: 2.8190 - regression_loss: 2.2355 - classification_loss: 0.5836
Epoch 2/50
79/79 [==============================] - ETA: 0s - loss: 2.5292 - regression_loss: 2.0832 - classification_loss: 0.4460
Epoch 00002: saving model to ./snapshots/resnet50_csv_02.h5
79/79 [==============================] - 358s 5s/step - loss: 2.5292 - regression_loss: 2.0832 - classification_loss: 0.4460
Epoch 3/50
79/79 [==============================] - ETA: 0s - loss: 2.4494 - regression_loss: 2.0271 - classification_loss: 0.4223
Epoch 00003: saving model to ./snapshots/resnet50_csv_03.h5
79/79 [==============================] - 355s 4s/step - loss: 2.4494 - regression_loss: 2.0271 - classification_loss: 0.4223
Epoch 4/50
79/79 [==============================] - ETA: 0s - loss: 2.4016 - regression_loss: 1.9924 - classification_loss: 0.4092
Epoch 00004: saving model to ./snapshots/resnet50_csv_04.h5
79/79 [==============================] - 352s 4s/step - loss: 2.4016 - regression_loss: 1.9924 - classification_loss: 0.4092
Epoch 5/50
79/79 [==============================] - ETA: 0s - loss: 2.3676 - regression_loss: 1.9648 - classification_loss: 0.4028
Epoch 00005: saving model to ./snapshots/resnet50_csv_05.h5
79/79 [==============================] - 350s 4s/step - loss: 2.3676 - regression_loss: 1.9648 - classification_loss: 0.4028
Epoch 6/50
79/79 [==============================] - ETA: 0s - loss: 2.3379 - regression_loss: 1.9460 - classification_loss: 0.3919
Epoch 00006: saving model to ./snapshots/resnet50_csv_06.h5
79/79 [==============================] - 348s 4s/step - loss: 2.3379 - regression_loss: 1.9460 - classification_loss: 0.3919
Epoch 7/50
79/79 [==============================] - ETA: 0s - loss: 2.3393 - regression_loss: 1.9445 - classification_loss: 0.3947
Epoch 00007: saving model to ./snapshots/resnet50_csv_07.h5
79/79 [==============================] - 350s 4s/step - loss: 2.3393 - regression_loss: 1.9445 - classification_loss: 0.3947
Epoch 8/50
79/79 [==============================] - ETA: 0s - loss: 2.2852 - regression_loss: 1.9043 - classification_loss: 0.3809
Epoch 00008: saving model to ./snapshots/resnet50_csv_08.h5
79/79 [==============================] - 348s 4s/step - loss: 2.2852 - regression_loss: 1.9043 - classification_loss: 0.3809
Epoch 9/50
79/79 [==============================] - ETA: 0s - loss: 2.2881 - regression_loss: 1.9026 - classification_loss: 0.3855
Epoch 00009: saving model to ./snapshots/resnet50_csv_09.h5
79/79 [==============================] - 346s 4s/step - loss: 2.2881 - regression_loss: 1.9026 - classification_loss: 0.3855
Epoch 10/50
79/79 [==============================] - ETA: 0s - loss: 2.2786 - regression_loss: 1.8964 - classification_loss: 0.3821
Epoch 00010: saving model to ./snapshots/resnet50_csv_10.h5
79/79 [==============================] - 349s 4s/step - loss: 2.2786 - regression_loss: 1.8964 - classification_loss: 0.3821
Epoch 11/50
79/79 [==============================] - ETA: 0s - loss: 2.2696 - regression_loss: 1.8890 - classification_loss: 0.3807
Epoch 00011: saving model to ./snapshots/resnet50_csv_11.h5
79/79 [==============================] - 353s 4s/step - loss: 2.2696 - regression_loss: 1.8890 - classification_loss: 0.3807
Epoch 12/50
79/79 [==============================] - ETA: 0s - loss: 2.2538 - regression_loss: 1.8793 - classification_loss: 0.3745
Epoch 00012: saving model to ./snapshots/resnet50_csv_12.h5
79/79 [==============================] - 350s 4s/step - loss: 2.2538 - regression_loss: 1.8793 - classification_loss: 0.3745
Epoch 13/50
79/79 [==============================] - ETA: 0s - loss: 2.2380 - regression_loss: 1.8656 - classification_loss: 0.3724
Epoch 00013: saving model to ./snapshots/resnet50_csv_13.h5
79/79 [==============================] - 348s 4s/step - loss: 2.2380 - regression_loss: 1.8656 - classification_loss: 0.3724
Epoch 14/50
79/79 [==============================] - ETA: 0s - loss: 2.2352 - regression_loss: 1.8640 - classification_loss: 0.3712
Epoch 00014: saving model to ./snapshots/resnet50_csv_14.h5
79/79 [==============================] - 349s 4s/step - loss: 2.2352 - regression_loss: 1.8640 - classification_loss: 0.3712
Epoch 15/50
79/79 [==============================] - ETA: 0s - loss: 2.2268 - regression_loss: 1.8569 - classification_loss: 0.3699
Epoch 00015: saving model to ./snapshots/resnet50_csv_15.h5
79/79 [==============================] - 345s 4s/step - loss: 2.2268 - regression_loss: 1.8569 - classification_loss: 0.3699
Epoch 16/50
79/79 [==============================] - ETA: 0s - loss: 2.2111 - regression_loss: 1.8465 - classification_loss: 0.3646
Epoch 00016: saving model to ./snapshots/resnet50_csv_16.h5
79/79 [==============================] - 349s 4s/step - loss: 2.2111 - regression_loss: 1.8465 - classification_loss: 0.3646
Epoch 17/50
79/79 [==============================] - ETA: 0s - loss: 2.2176 - regression_loss: 1.8472 - classification_loss: 0.3704
Epoch 00017: saving model to ./snapshots/resnet50_csv_17.h5
79/79 [==============================] - 354s 4s/step - loss: 2.2176 - regression_loss: 1.8472 - classification_loss: 0.3704
Epoch 18/50
79/79 [==============================] - ETA: 0s - loss: 2.2032 - regression_loss: 1.8372 - classification_loss: 0.3660
Epoch 00018: saving model to ./snapshots/resnet50_csv_18.h5
79/79 [==============================] - 350s 4s/step - loss: 2.2032 - regression_loss: 1.8372 - classification_loss: 0.3660
Epoch 19/50
79/79 [==============================] - ETA: 0s - loss: 2.1871 - regression_loss: 1.8223 - classification_loss: 0.3648
Epoch 00019: saving model to ./snapshots/resnet50_csv_19.h5
79/79 [==============================] - 353s 4s/step - loss: 2.1871 - regression_loss: 1.8223 - classification_loss: 0.3648
Epoch 20/50
79/79 [==============================] - ETA: 0s - loss: 2.1950 - regression_loss: 1.8287 - classification_loss: 0.3663
Epoch 00020: saving model to ./snapshots/resnet50_csv_20.h5
79/79 [==============================] - 352s 4s/step - loss: 2.1950 - regression_loss: 1.8287 - classification_loss: 0.3663
Epoch 21/50
79/79 [==============================] - ETA: 0s - loss: 2.1981 - regression_loss: 1.8314 - classification_loss: 0.3667
Epoch 00021: saving model to ./snapshots/resnet50_csv_21.h5

Epoch 00021: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.
79/79 [==============================] - 349s 4s/step - loss: 2.1981 - regression_loss: 1.8314 - classification_loss: 0.3667
Epoch 22/50
79/79 [==============================] - ETA: 0s - loss: 2.1193 - regression_loss: 1.7752 - classification_loss: 0.3441
Epoch 00022: saving model to ./snapshots/resnet50_csv_22.h5
79/79 [==============================] - 345s 4s/step - loss: 2.1193 - regression_loss: 1.7752 - classification_loss: 0.3441
Epoch 23/50
79/79 [==============================] - ETA: 0s - loss: 2.1188 - regression_loss: 1.7757 - classification_loss: 0.3431
Epoch 00023: saving model to ./snapshots/resnet50_csv_23.h5
79/79 [==============================] - 344s 4s/step - loss: 2.1188 - regression_loss: 1.7757 - classification_loss: 0.3431
Epoch 24/50
79/79 [==============================] - ETA: 0s - loss: 2.0958 - regression_loss: 1.7562 - classification_loss: 0.3396
Epoch 00024: saving model to ./snapshots/resnet50_csv_24.h5
79/79 [==============================] - 345s 4s/step - loss: 2.0958 - regression_loss: 1.7562 - classification_loss: 0.3396
Epoch 25/50
79/79 [==============================] - ETA: 0s - loss: 2.1147 - regression_loss: 1.7724 - classification_loss: 0.3423
Epoch 00025: saving model to ./snapshots/resnet50_csv_25.h5
79/79 [==============================] - 343s 4s/step - loss: 2.1147 - regression_loss: 1.7724 - classification_loss: 0.3423
Epoch 26/50
79/79 [==============================] - ETA: 0s - loss: 2.0993 - regression_loss: 1.7582 - classification_loss: 0.3411
Epoch 00026: saving model to ./snapshots/resnet50_csv_26.h5

Epoch 00026: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.
79/79 [==============================] - 344s 4s/step - loss: 2.0993 - regression_loss: 1.7582 - classification_loss: 0.3411
Epoch 27/50
79/79 [==============================] - ETA: 0s - loss: 2.0889 - regression_loss: 1.7505 - classification_loss: 0.3383
Epoch 00027: saving model to ./snapshots/resnet50_csv_27.h5
79/79 [==============================] - 339s 4s/step - loss: 2.0889 - regression_loss: 1.7505 - classification_loss: 0.3383
Epoch 28/50
79/79 [==============================] - ETA: 0s - loss: 2.0877 - regression_loss: 1.7499 - classification_loss: 0.3378
Epoch 00028: saving model to ./snapshots/resnet50_csv_28.h5
79/79 [==============================] - 339s 4s/step - loss: 2.0877 - regression_loss: 1.7499 - classification_loss: 0.3378
Epoch 29/50
79/79 [==============================] - ETA: 0s - loss: 2.0900 - regression_loss: 1.7523 - classification_loss: 0.3377
Epoch 00029: saving model to ./snapshots/resnet50_csv_29.h5
79/79 [==============================] - 338s 4s/step - loss: 2.0900 - regression_loss: 1.7523 - classification_loss: 0.3377
Epoch 30/50
79/79 [==============================] - ETA: 0s - loss: 2.0871 - regression_loss: 1.7493 - classification_loss: 0.3378
Epoch 00030: saving model to ./snapshots/resnet50_csv_30.h5
79/79 [==============================] - 337s 4s/step - loss: 2.0871 - regression_loss: 1.7493 - classification_loss: 0.3378
Epoch 31/50
79/79 [==============================] - ETA: 0s - loss: 2.0883 - regression_loss: 1.7507 - classification_loss: 0.3376
Epoch 00031: saving model to ./snapshots/resnet50_csv_31.h5
79/79 [==============================] - 337s 4s/step - loss: 2.0883 - regression_loss: 1.7507 - classification_loss: 0.3376
Epoch 32/50
79/79 [==============================] - ETA: 0s - loss: 2.0953 - regression_loss: 1.7558 - classification_loss: 0.3395
Epoch 00032: saving model to ./snapshots/resnet50_csv_32.h5

Epoch 00032: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.
79/79 [==============================] - 338s 4s/step - loss: 2.0953 - regression_loss: 1.7558 - classification_loss: 0.3395
Epoch 33/50
79/79 [==============================] - ETA: 0s - loss: 2.0827 - regression_loss: 1.7462 - classification_loss: 0.3366
Epoch 00033: saving model to ./snapshots/resnet50_csv_33.h5
79/79 [==============================] - 336s 4s/step - loss: 2.0827 - regression_loss: 1.7462 - classification_loss: 0.3366
Epoch 34/50
79/79 [==============================] - ETA: 0s - loss: 2.0999 - regression_loss: 1.7624 - classification_loss: 0.3375
Epoch 00034: saving model to ./snapshots/resnet50_csv_34.h5
79/79 [==============================] - 338s 4s/step - loss: 2.0999 - regression_loss: 1.7624 - classification_loss: 0.3375
Epoch 35/50
79/79 [==============================] - ETA: 0s - loss: 2.0895 - regression_loss: 1.7511 - classification_loss: 0.3384
Epoch 00035: saving model to ./snapshots/resnet50_csv_35.h5

Epoch 00035: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08.
79/79 [==============================] - 338s 4s/step - loss: 2.0895 - regression_loss: 1.7511 - classification_loss: 0.3384
Epoch 36/50
79/79 [==============================] - ETA: 0s - loss: 2.0795 - regression_loss: 1.7436 - classification_loss: 0.3360
Epoch 00036: saving model to ./snapshots/resnet50_csv_36.h5
79/79 [==============================] - 340s 4s/step - loss: 2.0795 - regression_loss: 1.7436 - classification_loss: 0.3360
Epoch 37/50
79/79 [==============================] - ETA: 0s - loss: 2.0797 - regression_loss: 1.7421 - classification_loss: 0.3376
Epoch 00037: saving model to ./snapshots/resnet50_csv_37.h5
79/79 [==============================] - 339s 4s/step - loss: 2.0797 - regression_loss: 1.7421 - classification_loss: 0.3376
Epoch 38/50
79/79 [==============================] - ETA: 0s - loss: 2.0824 - regression_loss: 1.7444 - classification_loss: 0.3380
Epoch 00038: saving model to ./snapshots/resnet50_csv_38.h5

Epoch 00038: ReduceLROnPlateau reducing learning rate to 9.999999939225292e-10.
79/79 [==============================] - 340s 4s/step - loss: 2.0824 - regression_loss: 1.7444 - classification_loss: 0.3380
Epoch 39/50
79/79 [==============================] - ETA: 0s - loss: 2.0828 - regression_loss: 1.7467 - classification_loss: 0.3360
Epoch 00039: saving model to ./snapshots/resnet50_csv_39.h5
79/79 [==============================] - 342s 4s/step - loss: 2.0828 - regression_loss: 1.7467 - classification_loss: 0.3360
Epoch 40/50
79/79 [==============================] - ETA: 0s - loss: 2.0845 - regression_loss: 1.7462 - classification_loss: 0.3384
Epoch 00040: saving model to ./snapshots/resnet50_csv_40.h5

Epoch 00040: ReduceLROnPlateau reducing learning rate to 9.999999717180686e-11.
79/79 [==============================] - 340s 4s/step - loss: 2.0845 - regression_loss: 1.7462 - classification_loss: 0.3384
Epoch 41/50
79/79 [==============================] - ETA: 0s - loss: 2.0895 - regression_loss: 1.7528 - classification_loss: 0.3367
Epoch 00041: saving model to ./snapshots/resnet50_csv_41.h5
79/79 [==============================] - 340s 4s/step - loss: 2.0895 - regression_loss: 1.7528 - classification_loss: 0.3367
Epoch 42/50
79/79 [==============================] - ETA: 0s - loss: 2.0999 - regression_loss: 1.7614 - classification_loss: 0.3385
Epoch 00042: saving model to ./snapshots/resnet50_csv_42.h5

Epoch 00042: ReduceLROnPlateau reducing learning rate to 9.99999943962493e-12.
79/79 [==============================] - 341s 4s/step - loss: 2.0999 - regression_loss: 1.7614 - classification_loss: 0.3385
Epoch 43/50
79/79 [==============================] - ETA: 0s - loss: 2.0776 - regression_loss: 1.7421 - classification_loss: 0.3355
Epoch 00043: saving model to ./snapshots/resnet50_csv_43.h5
79/79 [==============================] - 341s 4s/step - loss: 2.0776 - regression_loss: 1.7421 - classification_loss: 0.3355
Epoch 44/50
79/79 [==============================] - ETA: 0s - loss: 2.0792 - regression_loss: 1.7417 - classification_loss: 0.3374
Epoch 00044: saving model to ./snapshots/resnet50_csv_44.h5
79/79 [==============================] - 339s 4s/step - loss: 2.0792 - regression_loss: 1.7417 - classification_loss: 0.3374
Epoch 45/50
79/79 [==============================] - ETA: 0s - loss: 2.0868 - regression_loss: 1.7491 - classification_loss: 0.3377
Epoch 00045: saving model to ./snapshots/resnet50_csv_45.h5

Epoch 00045: ReduceLROnPlateau reducing learning rate to 9.999999092680235e-13.
79/79 [==============================] - 336s 4s/step - loss: 2.0868 - regression_loss: 1.7491 - classification_loss: 0.3377
Epoch 46/50
79/79 [==============================] - ETA: 0s - loss: 2.0871 - regression_loss: 1.7478 - classification_loss: 0.3393
Epoch 00046: saving model to ./snapshots/resnet50_csv_46.h5
79/79 [==============================] - 345s 4s/step - loss: 2.0871 - regression_loss: 1.7478 - classification_loss: 0.3393
Epoch 47/50
79/79 [==============================] - ETA: 0s - loss: 2.0867 - regression_loss: 1.7487 - classification_loss: 0.3380
Epoch 00047: saving model to ./snapshots/resnet50_csv_47.h5

Epoch 00047: ReduceLROnPlateau reducing learning rate to 9.9999988758398e-14.
79/79 [==============================] - 342s 4s/step - loss: 2.0867 - regression_loss: 1.7487 - classification_loss: 0.3380
Epoch 48/50
79/79 [==============================] - ETA: 0s - loss: 2.1018 - regression_loss: 1.7635 - classification_loss: 0.3383
Epoch 00048: saving model to ./snapshots/resnet50_csv_48.h5
79/79 [==============================] - 338s 4s/step - loss: 2.1018 - regression_loss: 1.7635 - classification_loss: 0.3383
Epoch 49/50
79/79 [==============================] - ETA: 0s - loss: 2.0916 - regression_loss: 1.7522 - classification_loss: 0.3394
Epoch 00049: saving model to ./snapshots/resnet50_csv_49.h5

Epoch 00049: ReduceLROnPlateau reducing learning rate to 9.999999146890344e-15.
79/79 [==============================] - 341s 4s/step - loss: 2.0916 - regression_loss: 1.7522 - classification_loss: 0.3394
Epoch 50/50
79/79 [==============================] - ETA: 0s - loss: 2.0823 - regression_loss: 1.7463 - classification_loss: 0.3359
Epoch 00050: saving model to ./snapshots/resnet50_csv_50.h5
79/79 [==============================] - 340s 4s/step - loss: 2.0823 - regression_loss: 1.7463 - classification_loss: 0.3359

!keras_retinanet/bin/evaluate.py  csv annotate.csv class.csv /content/keras-retinanet/snapshots/resnet50_csv_50.h5 --convert-model

2020-12-17 02:09:56.093543: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
Using TensorFlow backend.
Loading model, this may take a second...
2020-12-17 02:09:59.689283: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2020-12-17 02:09:59.692255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:09:59.692785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-12-17 02:09:59.692818: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-12-17 02:09:59.694644: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-12-17 02:09:59.696510: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-12-17 02:09:59.696904: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-12-17 02:09:59.698827: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-12-17 02:09:59.699900: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-12-17 02:09:59.703698: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-12-17 02:09:59.703799: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:09:59.704266: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:09:59.704696: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-12-17 02:09:59.710138: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 2200000000 Hz
2020-12-17 02:09:59.710387: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2ce8bc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-12-17 02:09:59.710415: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-12-17 02:09:59.804752: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:09:59.805451: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2ce8d80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-12-17 02:09:59.805481: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0
2020-12-17 02:09:59.805687: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:09:59.806096: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s
2020-12-17 02:09:59.806131: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-12-17 02:09:59.806169: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2020-12-17 02:09:59.806216: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2020-12-17 02:09:59.806229: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2020-12-17 02:09:59.806242: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2020-12-17 02:09:59.806256: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2020-12-17 02:09:59.806271: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-12-17 02:09:59.806320: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:09:59.806770: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:09:59.807151: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2020-12-17 02:09:59.807187: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2020-12-17 02:10:00.414097: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-12-17 02:10:00.414155: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263]      0 
2020-12-17 02:10:00.414166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0:   N 
2020-12-17 02:10:00.414344: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:10:00.414851: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-12-17 02:10:00.415264: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2020-12-17 02:10:00.415298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10123 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)
Running network: N/A% (0 of 2519) |       | Elapsed Time: 0:00:00 ETA:  --:--:--2020-12-17 02:10:05.248460: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2020-12-17 02:10:06.296181: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
Running network: 100% (2519 of 2519) |####| Elapsed Time: 0:03:51 Time:  0:03:51
Parsing annotations: 100% (2519 of 2519) || Elapsed Time: 0:00:00 Time:  0:00:00
4000 instances of class Pneumonia with average precision: 0.4814
Inference time for 2519 images: 0.0552
mAP using the weighted average of precisions among classes: 0.4814
mAP: 0.4814

from keras_retinanet import models
from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from keras_retinanet.utils.visualization import draw_box, draw_caption
from keras_retinanet.utils.colors import label_color
import matplotlib.pyplot as plt
import cv2

model_path='/content/keras-retinanet/snapshots/resnet50_csv_50.h5'
model = models.load_model(model_path, backbone_name='resnet50')
model = models.convert_model(model)

#model.save("Renitanet_ITR3.h5")

THRES_SCORE = 0.4

def draw_image(patient_id):
  pred_box=[]
  actual_box=[]
  file_path = out_path + patient_id+'.jpg'
  image = read_image_bgr(file_path)

  draw = image.copy()
  draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)

  image = preprocess_image(image)
  image, scale = resize_image(image)

  pred_boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))

  pred_boxes /= scale
  
  data = train_dict[patient_id]
  actual_boxes = data['boxes']
  #print(actual_boxes)  
  for box, score, label in zip(pred_boxes[0], scores[0], labels[0]):
      if score < THRES_SCORE:
          break
      #print(box)
      color = label_color(label)
      #print(color)
      b = box.astype(int)
      pred_box.append(np.array(b).tolist())
      draw_box(draw, b, color=color)
      caption = "{} {:.2f}".format('pnuemonia', score)
      draw_caption(draw, b, caption)
  for box in actual_boxes:
     box = [int(b) for b in box]
     x1, y1, width, height = box
     y2 = y1 + height
     x2 = x1 + width
     box = [x1,y1,x2,y2]
     actual_box.append(box)
     draw_box(draw, box, color=cv2.IMREAD_COLOR)
  iou_list=[]
  for box_a in pred_box:
    for box_b in actual_box:
       iou=bb_intersection_over_union(box_a, box_b)
       iou_list.append(iou)
  mean_iou = sum(iou_list)/len(actual_box)
  print("Mean_IOU::" + str(mean_iou))
  plt.figure(figsize=(10, 10))
  plt.axis('off')
  plt.imshow(draw)
  plt.show()

def bb_intersection_over_union(boxA, boxB):
	# determine the (x, y)-coordinates of the intersection rectangle
	xA = max(boxA[0], boxB[0])
	yA = max(boxA[1], boxB[1])
	xB = min(boxA[2], boxB[2])
	yB = min(boxA[3], boxB[3])
 
	# compute the area of intersection rectangle
	interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
 
	# compute the area of both the prediction and ground-truth
	# rectangles
	boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
	boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
 
	# compute the intersection over union by taking the intersection
	# area and dividing it by the sum of prediction + ground-truth
	# areas - the interesection area
	iou = interArea / float(boxAArea + boxBArea - interArea)
 
	# return the intersection over union value
	return iou

#Black Boxes - Actual
#Blue Boxes  - Predicted
THRES_SCORE = 0.4
patient_id='4f146c9c-d0f0-41ae-b9df-e0f7a7c75328'
draw_image(patient_id)

Mean_IOU::0.5820364793965926

THRES_SCORE = 0.4
patient_id='4f7604aa-98ef-43c5-a0f4-6b4b043bd7dd'
draw_image(patient_id)

Mean_IOU::0.7589222000706671

THRES_SCORE = 0.4
patient_id='8192a218-3ed8-450a-bb3e-052e36567763'
draw_image(patient_id)

Mean_IOU::0.5465623167716114

THRES_SCORE =0.4
patient_id='815dd1c1-3e01-4038-9723-39d05e8b3cd3'
draw_image(patient_id)

Mean_IOU::0.7782411382313259

THRES_SCORE = 0.4
patient_id='504cbf6f-b20a-4a8b-8d78-e4016bada396'
draw_image(patient_id)

Mean_IOU::0.7199173489842614

Iteration 3:¶

Learning Rate = 1e-4
No of Epochs = 50

Results: • In this Iteration, we decrease the Learning rate from 1e-3 to 1e-4. • After the 43rd Epoch the Classification Loss value reaches to 0.3355 • Again, it starts to Increase the Loss Value to 0.3374, Likewise It fluctuates the loss values between 0.3355 to 0.3394. • At last, Finally Classification Value reaches to the 0.335 and Given the MAP Value is 0.4814 • This Iteration gave the Best IOU Values between 0.5 to 0.7.

	patientId	x	y	width	height	Target
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1
5	00436515-870c-4b36-a041-de91049b9ab4	562.0	152.0	256.0	453.0	1
6	00569f44-917d-4c86-a842-81832af98c30	NaN	NaN	NaN	NaN	0
7	006cec2e-6ce2-4549-bffa-eadfcd1e9970	NaN	NaN	NaN	NaN	0
8	00704310-78a8-4b38-8475-49f4573b2dbb	323.0	577.0	160.0	104.0	1
9	00704310-78a8-4b38-8475-49f4573b2dbb	695.0	575.0	162.0	137.0	1

	patientId	x	y	width	height	Target	aspect_ratio	area	age	gender
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0	NaN	NaN	51	F
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0	NaN	NaN	48	F
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0	NaN	NaN	19	M
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0	NaN	NaN	28	M
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1	0.562005	80727.0	32	F

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label	0	0	0	0	1	0	0	1	0	0	0	0	1	1	0	1	0	1	0	1	0	1	0	0	0	1	0	0	1	0	0	0	1	1	0	1	0	0	1	1	...	1	0	0	0	0	0	0	1	0	0	1	0	1	0	0	0	0	1	1	1	1	1	0	1	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	1
boxes	[]	[]	[]	[]	[[152.0, 264.0, 379.0, 213.0], [152.0, 562.0, ...	[]	[]	[[577.0, 323.0, 104.0, 160.0], [575.0, 695.0, ...	[]	[]	[]	[]	[[322.0, 288.0, 135.0, 94.0], [299.0, 547.0, 1...	[[544.0, 306.0, 244.0, 168.0], [511.0, 650.0, ...	[]	[[184.0, 181.0, 506.0, 206.0], [275.0, 571.0, ...	[]	[[416.0, 703.0, 77.0, 84.0]]	[]	[[437.0, 652.0, 293.0, 161.0], [405.0, 301.0, ...	[]	[[613.0, 133.0, 275.0, 275.0], [427.0, 678.0, ...	[]	[]	[]	[[182.0, 155.0, 501.0, 273.0], [220.0, 599.0, ...	[]	[]	[[318.0, 229.0, 301.0, 250.0], [216.0, 604.0, ...	[]	[]	[]	[[306.0, 141.0, 327.0, 225.0], [285.0, 609.0, ...	[[582.0, 214.0, 133.0, 239.0], [540.0, 664.0, ...	[]	[[415.0, 225.0, 101.0, 98.0]]	[]	[]	[[289.0, 366.0, 527.0, 208.0], [278.0, 714.0, ...	[[626.0, 535.0, 240.0, 177.0], [825.0, 175.0, ...	...	[[555.0, 606.0, 129.0, 156.0]]	[]	[]	[]	[]	[]	[]	[[395.0, 662.0, 488.0, 253.0], [402.0, 220.0, ...	[]	[]	[[742.0, 753.0, 241.0, 185.0], [165.0, 630.0, ...	[]	[[258.0, 543.0, 347.0, 175.0], [285.0, 84.0, 4...	[]	[]	[]	[]	[[253.0, 673.0, 322.0, 183.0]]	[[355.0, 606.0, 368.0, 216.0]]	[[218.0, 131.0, 379.0, 214.0], [127.0, 497.0, ...	[[265.0, 601.0, 383.0, 173.0], [370.0, 173.0, ...	[[447.0, 578.0, 258.0, 157.0], [268.0, 239.0, ...	[]	[[356.0, 622.0, 128.0, 149.0]]	[]	[]	[[386.0, 646.0, 206.0, 141.0], [473.0, 295.0, ...	[]	[]	[]	[]	[[416.0, 269.0, 285.0, 193.0], [475.0, 766.0, ...	[]	[]	[]	[[418.0, 666.0, 223.0, 186.0], [504.0, 316.0, ...	[[464.0, 609.0, 284.0, 240.0], [298.0, 185.0, ...	[]	[]	[[393.0, 570.0, 345.0, 261.0], [424.0, 233.0, ...

	x	y	width	height	Target	aspect_ratio	area	age	gender	class	patientId
0	NaN	NaN	NaN	NaN	0	NaN	NaN	51	F	No Lung Opacity / Not Normal	0004cfab-14fd-4e49-80ba-63a80b6bddd6
1	NaN	NaN	NaN	NaN	0	NaN	NaN	48	F	No Lung Opacity / Not Normal	00313ee0-9eaa-42f4-b0ab-c148ed3241cd
2	NaN	NaN	NaN	NaN	0	NaN	NaN	19	M	No Lung Opacity / Not Normal	00322d4d-1c29-4943-afc9-b6754be640eb
3	NaN	NaN	NaN	NaN	0	NaN	NaN	28	M	Normal	003d8fa0-6bf1-40ed-b54c-ac657f8495c5
4	264.0	152.0	213.0	379.0	1	0.562005	80727.0	32	F	Lung Opacity	00436515-870c-4b36-a041-de91049b9ab4

	image_dir	Target
0	../content/drive/My Drive/Colab Notebooks/caps...	0
1	../content/drive/My Drive/Colab Notebooks/caps...	0
2	../content/drive/My Drive/Colab Notebooks/caps...	0
3	../content/drive/My Drive/Colab Notebooks/caps...	1
4	../content/drive/My Drive/Colab Notebooks/caps...	0

	area	aspect_ratio	height	width	y	x	gender	age	Target	patientId
Total	20672.000000	20672.000000	20672.000000	20672.000000	20672.000000	20672.000000	0.0	0.0	0.0	0.0
Percent	68.389188	68.389188	68.389188	68.389188	68.389188	68.389188	0.0	0.0	0.0	0.0